bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-paced Convolutional Neural Networks
Authors: Li Hao , Gong Maoguo
Published: July 2017

Convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks. In order to distinguish the reliable data from the noisy and confusing data, we improve CNNs with self-paced learning (SPL) for enhancing the learning robustness of CNNs. In the proposed self-paced convolutional network (SPCN), each sample is assigned to a weight to reflect the easiness of the sample. Then a dynamic self-paced function is incorporated into the leaning objective of CNN to jointly learn the parameters of CNN and the latent weight variable. SPCN learns the samples from easy to complex and the sample weights can dynamically control the learning rates for converging to better values. To gain more insights of SPCN, theoretical studies are conducted to show that SPCN converges to a stationary solution and is robust to the noisy and confusing data. Experimental results on MNIST and rectangles datasets demonstrate that the proposed method outperforms baseline methods.
Large-scale Subspace Clustering by Fast Regression Coding
Authors: Li Jun , Zhao Handong , Tao Zhiqiang , Fu Yun
Published: July 2017

Large-Scale Subspace Clustering (LSSC) is an interesting and important problem in big data era. However, most existing methods (i.e., sparse or low-rank subspace clustering) cannot be directly used for solving LSSC because they suffer from the high time complexity-quadratic or cubic in n (the number of data points). To overcome this limitation, we propose a Fast Regression Coding (FRC) to optimize regression codes, and simultaneously train a non-linear function to approximate the codes. By using FRC, we develop an efficient Regression Coding Clustering (RCC) framework to solve the LSSC problem. It consists of sampling, FRC and clustering. RCC randomly samples a small number of data points, quickly calculates the codes of all data points by using the non-linear function learned from FRC, and employs a large-scale spectral clustering method to cluster the codes. Besides, we provide a theorem guarantee that the non-linear function has a first-order approximation ability and a group effect. The theorem manifests that the codes are easily used to construct a dividable similarity graph. Compared with the state-of-the-art LSSC methods, our model achieves better clustering results in large-scale datasets.
Affinity Learning for Mixed Data Clustering
Authors: Li Nan , Latecki Longin Jan
Published: July 2017

In this paper, we propose a novel affinity learning based framework for mixed data clustering, which includes: how to process data with mixed-type attributes, how to learn affinities between data points, and how to exploit the learned affinities for clustering. In the proposed framework, each original data attribute is represented with several abstract objects defined according to the specific data type and values. Each attribute value is transformed into the initial affinities between the data point and the abstract objects of attribute. We refine these affinities and infer the unknown affinities between data points by taking into account the interconnections among the attribute values of all data points. The inferred affinities between data points can be exploited for clustering. Alternatively, the refined affinities between data points and the abstract objects of attributes can be transformed into new data features for clustering. Experimental results on many real world data sets demonstrate that the proposed framework is effective for mixed data clustering.
Self-paced Compensatory Deep Boltzmann Machine for Semi-Structured Document Embedding
Authors: Li Shuangyin , Pan Rong , Yan Jun
Published: July 2017

In the last decade, there has been a huge amount of documents with different types of rich metadata information, which belongs to the Semi-Structured Documents (SSDs), appearing in many real applications. It is an interesting research work to model this type of text data following the way how humans understand text with informative metadata. In the paper, we introduce a Self-paced Compensatory Deep Boltzmann Machine (SCDBM) architecture that learns a deep neural network by using metadata information to learn deep structure layer-wisely for Semi-Structured Documents (SSDs) embedding in a self-paced way. Inspired by the way how humans understand text, the model defines a deep process of document vector extraction beyond the space of words by jointing the metadata where each layer selects different types of metadata. We present efficient learning and inference algorithms for the SCDBM model and empirically demonstrate that using the representation discovered by this model has better performance on semi-structured document classification and retrieval, and tag prediction comparing with state-of-the-art baselines.
MAM-RNN: Multi-level Attention Model Based RNN for Video Captioning
Authors: Li Xuelong , Zhao Bin , Lu Xiaoqiang
Published: July 2017

Visual information is quite important for the task of video captioning. However, in the video, there are a lot of uncorrelated content, which may cause interference to generate a correct caption. Based on this point, we attempt to exploit the visual features which are most correlated to the caption. In this paper, a Multi-level Attention Model based Recurrent Neural Network (MAM-RNN) is proposed, where MAM is utilized to encode the visual feature and RNN works as the decoder to generate the video caption. During generation, the proposed approach is able to adaptively attend to the salient regions in the frame and the frames correlated to the caption. Practically, the experimental results on two benchmark datasets, i.e., MSVD and Charades, have shown the excellent performance of the proposed approach.
Incomplete Attribute Learning with auxiliary labels
Authors: Liang Kongming , Guo Yuhong , Chang Hong , Chen Xilin
Published: July 2017

Visual attribute learning is a fundamental and challenging problem for image understanding. Considering the huge semantic space of attributes, it is economically impossible to annotate all their presence or absence for a natural image via crowd-sourcing. In this paper, we tackle the incompleteness nature of visual attributes by introducing auxiliary labels into a novel transductive learning framework. By jointly predicting the attributes from the input images and modeling the relationship of attributes and auxiliary labels, the missing attributes can be recovered effectively. In addition, the proposed model can be solved efficiently in an alternative way by optimizing quadratic programming problems and updating parameters in closed-form solutions. Moreover, we propose and investigate different methods for acquiring auxiliary labels. We conduct experiments on three widely used attribute prediction datasets. The experimental results show that our proposed method can achieve the state-of-the-art performance with access to partially observed attribute annotations.
LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks
Authors: Likhyani Ankita , Bedathur Srikanta , P Deepak
Published: July 2017

Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we focus on the problem of location promotion and develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a joint model called LoCaTe, consisting of (i) user mobility model estimated using kernel density estimates; (ii) a model of the semantics of the location using topic models; and (iii) a model of time-gap between check-ins using exponential distribution. We validate our model on a long-term crawl of Foursquare data collected between Jan 2015 Feb 2016, as well as on publicly available LBSN datasets. Our experiments demonstrate that LoCaTe significantly outperforms state-of-the-art models for the same task.
Hybrid Neural Networks for Learning the Trend in Time Series
Authors: Lin Tao , Guo Tian , Aberer Karl
Published: July 2017

The trend of time series characterizes the intermediate upward and downward behaviour of time series. Learning and forecasting the trend in time series data play an important role in many real applications, ranging from resource allocation in data centers, load schedule in smart grid, and so on. Inspired by the recent successes of neural networks, in this paper we propose TreNet, a novel end-to-end hybrid neural network to learn local and global contextual features for predicting the trend of time series. TreNet leverages convolutional neural networks (CNNs) to extract salient features from local raw data of time series. Meanwhile, considering the long-range dependency existing in the sequence of historical trends of time series, TreNet uses a long-short term memory recurrent neural network (LSTM) to capture such dependency. Then, a feature fusion layer is to learn joint representation for predicting the trend. TreNet demonstrates its effectiveness by outperforming CNN, LSTM, the cascade of CNN and LSTM, Hidden Markov Model based method and various kernel based baselines on real datasets.
Locally Linear Factorization Machines
Authors: Liu Chenghao , Zhang Teng , Zhao Peilin , Zhou Jun , Sun Jianling
Published: July 2017

Factorization Machines (FMs) are a widely used method for efficiently using high-order feature interactions in classification and regression tasks. Unfortunately, despite increasing interests in FMs, existing work only considers high order information of the input features which limits their capacities in non-linear problems and fails to capture the underlying structures of more complex data. In this work, we present a novel Locally Linear Factorization Machines (LLFM) which overcomes this limitation by exploring local coding technique. Unlike existing local coding classifiers that involve a phase of unsupervised anchor point learning and predefined local coding scheme which is suboptimal as the class label information is not exploited in discovering the encoding and thus can result in a suboptimal encoding for prediction, we formulate a joint optimization over the anchor points, local coding coordinates and FMs variables to minimize classification or regression risk. Empirically, we demonstrate that our approach achieves much better predictive accuracy than other competitive methods which employ LLFM with unsupervised anchor point learning and predefined local coding scheme.
Learning Concise Representations of Users' Influences through Online Behaviors
Authors: Liu Shenghua , Zheng Houdong , Shen Huawei , Cheng Xueqi , Liao Xiangwen
Published: July 2017

Whereas it is well known that social network users influence each other, a fundamental problem in influence maximization, opinion formation and viral marketing is that users' influences are difficult to quantify. Previous work has directly defined an independent model parameter to capture the interpersonal influence between each pair of users. However, such models do not consider how influences depend on each other if they originate from the same user or if they act on the same user. To do so, these models need a parameter for each pair of users, which results in high-dimensional models becoming easily trapped into the overfitting problem. Given these problems, another way of defining the parameters is needed to consider the dependencies. Thus we propose a model that defines parameters for every user with a latent influence vector and a susceptibility vector. Such low-dimensional and distributed representations naturally cause the interpersonal influences involving the same user to be coupled with each other, thus reducing the model's complexity. Additionally, the model can easily consider the sentimental polarities of users' messages and how sentiment affects users' influences. In this study, we conduct extensive experiments on real Microblog data, showing that our model with distributed representations achieves better accuracy than the state-of-the-art and pair-wise models, and that learning influences on sentiments benefit performance.
Learning User Dependencies for Recommendation
Authors: Liu Yong , Zhao Peilin , Liu Xin , Wu Min , Duan Lixin , Li Xiao-Li
Published: July 2017

Social recommender systems exploit users' social relationships to improve recommendation accuracy. Intuitively, a user tends to trust different people regarding with different scenarios. Therefore, one main challenge of social recommendation is to exploit the most appropriate dependencies between users for a given recommendation task. Previous social recommendation methods are usually developed based on pre-defined user dependencies. Thus, they may not be optimal for a specific recommendation task. In this paper, we propose a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which can automatically learn the dependencies between users to improve recommendation accuracy. In PRMF, users' latent features are assumed to follow a matrix variate normal (MVN) distribution. Both positive and negative user dependencies can be modeled by the row precision matrix of the MVN distribution. Moreover, we also propose an alternating optimization algorithm to solve the optimization problem of PRMF. Extensive experiments on four real datasets have been performed to demonstrate the effectiveness of the proposed PRMF model.
Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process
Authors: Luo Ling , Li Bin , Koprinska Irena , Berkovsky Shlomo , Chen Fang
Published: July 2017

Customer behavior modeling is important for businesses in order to understand, attract and retain customers. It is critical that the models are able to track the dynamics of customer behavior over time. We propose FC-CSM, a Customer Segmentation Model based on a Fragmentation-Coagulation process, which can track the evolution of customer segmentation, including the splitting and merging of customer groups. We conduct a case study using transaction data from a major Australian supermarket chain, where we: 1) show that our model achieves high fitness of purchase rate, outperforming models using mixture of Poisson processes; 2) compare the impact of promotions on customers for different products; and 3) track how customer groups evolve over time and how individual customers shift across groups. Our model provides valuable information to stakeholders about the different types of customers, how they change purchase behavior, and which customers are more receptive to promotion campaigns.
Near-Feasible Stable Matchings with Budget Constraints
Authors: Kawase Yasushi , Iwasaki Atsushi
Published: July 2017

This paper deals with two-sided matching with budget constraints where one side (firm or hospital) can make monetary transfers (offer wages) to the other (worker or doctor). In a standard model, while multiple doctors can be matched to a single hospital, a hospital has a maximum quota: the number of doctors assigned to a hospital cannot exceed a certain limit. In our model, a hospital instead has a fixed budget: the total amount of wages allocated by each hospital to doctors is constrained. With budget constraints, stable matchings may fail to exist and checking the existence is hard. To deal with the nonexistence of stable matchings, we extend the “matching with contracts” model by Hatfield and Milgrom, so that it handles near-feasible matchings that exceeds each budget of the hospitals by a certain amount. We then propose two novel mechanisms that efficiently return such a near-feasible matching that is stable with respect to the actual amount of wages allocated by each hospital. In particular, by sacrificing strategy-proofness, our second mechanism achieves the best possible bound.
On Subset Selection with General Cost Constraints
Authors: Qian Chao , Shi Jing-Cheng , Yu Yang , Tang Ke
Published: July 2017

This paper considers the subset selection problem with a monotone objective function and a monotone cost constraint, which relaxes the submodular property of previous studies. We first show that the approximation ratio of the generalized greedy algorithm is $\frac{\alpha}{2}(1 \textendash \frac{1}{e^{\alpha}})$ (where $\alpha$ is the submodularity ratio); and then propose POMC, an anytime randomized iterative approach that can utilize more time to find better solutions than the generalized greedy algorithm. We show that POMC can obtain the same general approximation guarantee as the generalized greedy algorithm, but can achieve better solutions in cases and applications.
Stacking With Auxiliary Features
Authors: Rajani Nazneen Fatema , Mooney Raymond J.
Published: July 2017

Ensembling methods are well known for improving prediction accuracy. However, they are limited in the sense that they cannot effectively discriminate among component models. In this paper, we propose stacking with auxiliary features that learns to fuse additional relevant information from multiple component systems as well as input instances to improve performance. We use two types of auxiliary features -- instance features and provenance features. The instance features enable the stacker to discriminate across input instances and the provenance features enable the stacker to discriminate across component systems. When combined together, our algorithm learns to rely on systems that not just agree on an output but also the provenance of this output in conjunction with the properties of the input instance. We demonstrate the success of our approach on three very different and challenging natural language and vision problems: Slot Filling, Entity Discovery and Linking, and ImageNet Object Detection. We obtain new state-of-the-art results on the first two tasks and significant improvements on the ImageNet task, thus verifying the power and generality of our approach.
Sense Beauty by Label Distribution Learning
Authors: Ren Yi , Geng Xin
Published: July 2017

Beauty is always an attractive topic in the human society, not only artists and psychologists, but also scientists have been searching for an answer -- what is beautiful. This paper presents an approach to learning the human sense toward facial beauty. Different from previous study, the human sense is represented by a label distribution, which covers the full range of beauty ratings and indicates the degree to which each beauty rating describes the face. The motivation is that the human sense of beauty is generally quite subjective, thus it might be inappropriate to represent it with a single scalar, as most previous work does. Therefore, we propose a method called Beauty Distribution Transformation(BDT) to covert the k-wise ratings to label distributions and propose a learning method called Structural Label Distribution Learning(SLDL) based on structural Support Vector Machine to learn the human sense of facial beauty.
Tosca: Operationalizing Commitments Over Information Protocols
Authors: King Thomas C. , Günay Akın , Chopra Amit K. , Singh Munindar P.
Published: July 2017

The notion of commitment is widely studied as a high-level abstraction for modeling multiagent interaction. An important challenge is supporting flexible decentralized enactments of commitment specifications. In this paper, we combine recent advances on specifying commitments and information protocols. Specifically, we contribute Tosca, a technique for automatically synthesizing information protocols from commitment specifications. Our main result is that the synthesized protocols support commitment alignment, which is the idea that agents must make compatible inferences about their commitments despite decentralization.
Locally Consistent Bayesian Network Scores for Multi-Relational Data
Authors: Schulte Oliver , Gholami Sajjad
Published: July 2017

An important task for relational learning is Bayesian network (BN) structure learning. A fundamental component of structure learning is a model selection score that measures how well a model fits a dataset. We describe a new method that upgrades for multi-relational databases, a log-linear BN score designed for single-table i.i.d. data. Chickering and Meek showed that for i.i.d. data, standard BN scores are locally consistent, meaning that their maxima converge to an optimal model, that represents the data generating distribution {\em and} contains no redundant edges. Our main theorem establishes that if a model selection score is locally consistent for i.i.d. data, then our upgraded gain function is locally consistent for relational data as well. To our knowledge this is the first consistency result for relational structure learning. A novel aspect of our approach is employing a {\em gain function} that compares two models: a current vs. an alternative BN structure. In contrast, previous approaches employed a score that is a function of a single model only. Empirical evaluation on six benchmark relational databases shows that our gain function is also practically useful: On realistic size data sets, it selects informative BN structures with a better data fit than those selected by baseline single-model scores.
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Authors: Shen Zebang , Qian Hui , Mu Tongzhou , Zhang Chao
Published: July 2017

Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications. In this paper, we propose a doubly stochastic algorithm with a novel accelerating multi-momentum technique to solve large scale empirical risk minimization problem for learning tasks. While enjoying a provably superior convergence rate, in each iteration, such algorithm only accesses a mini batch of samples and meanwhile updates a small block of variable coordinates, which substantially reduces the amount of memory reference when both the massive sample size and ultra-high dimensionality are involved. Specifically, to obtain an ε-accurate solution, our algorithm requires only O(log(1/ε)/sqrt(ε)) overall computation for the general convex case and O((n+sqrt{nκ})log(1/ε)) for the strongly convex case. Empirical studies on huge scale datasets are conducted to illustrate the efficiency of our method in practice.
Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
Authors: Sui Yanan , Burdick Joel W.
Published: July 2017

We consider sequential decision making under uncertainty, the optimization over large decision space with noisy comparative feedback. This problem can be formulated as a K-armed Dueling Bandits problem where K is the total number of decisions. When K is very large, existing dueling bandits algorithms suffer huge cumulative regret before converging on the optimal arm. This paper studies the dueling bandits problem with a large number of dependent arms. Our problem is motivated by a clinical decision making process in large decision space. We propose an efficient algorithm CorrDuel for the problem which makes decisions to simultaneously deliver effective therapy and explore the decision space. Many sequential decision making problems with large and structured decision space could be facilitated by our algorithm. After evaluated the fast convergence of CorrDuel in analysis and simulation experiments, we applied it on a live clinical trial of therapeutic spinal cord stimulation. It is the first applied algorithm towards spinal cord injury treatments and experimental results show the effectiveness and efficiency of our algorithm.
Student-t Process Regression with Student-t Likelihood
Authors: Tang Qingtao , Niu Li , Wang Yisen , Dai Tao , An Wangpeng , Cai Jianfei , Xia Shu-Tao
Published: July 2017

Gaussian Process Regression (GPR) is a powerful Bayesian method. However, the performance of GPR can be significantly degraded when the training data are contaminated by outliers, including target outliers and input outliers. Although there are some variants of GPR (e.g., GPR with Student-t likelihood (GPRT)) aiming to handle outliers, most of the variants focus on handling the target outliers while little effort has been done to deal with the input outliers. In contrast, in this work, we aim to handle both the target outliers and the input outliers at the same time. Specifically, we replace the Gaussian noise in GPR with independent Student-t noise to cope with the target outliers. Moreover, to enhance the robustness w.r.t. the input outliers, we use a Student-t Process prior instead of the common Gaussian Process prior, leading to Student-t Process Regression with Student-t Likelihood (TPRT). We theoretically show that TPRT is more robust to both input and target outliers than GPR and GPRT, and prove that both GPR and GPRT are special cases of TPRT. Various experiments demonstrate that TPRT outperforms GPR and its variants on both synthetic and real datasets.
Inverse Covariance Estimation with Structured Groups
Authors: Tao Shaozhe , Sun Yifan , Boley Daniel
Published: July 2017

Estimating the inverse covariance matrix of p variables from n observations is challenging when n is much less than p, since the sample covariance matrix is singular and cannot be inverted. A popular solution is to optimize for the L1 penalized estimator; however, this does not incorporate structure domain knowledge and can be expensive to optimize. We consider finding inverse covariance matrices with group structure, defined as potentially overlapping principal submatrices, determined from domain knowledge (e.g. categories or graph cliques). We propose a new estimator for this problem setting that can be derived efficiently via the conditional gradient method, leveraging chordal decomposition theory for scalability. Simulation results show significant improvement in sample complexity when the correct group structure is known. We also apply these estimators to 14,910 stock closing prices, with noticeable improvement when group sparsity is exploited.
From Ensemble Clustering to Multi-View Clustering
Authors: Tao Zhiqiang , Liu Hongfu , Li Sheng , Ding Zhengming , Fu Yun
Published: July 2017

Multi-View Clustering (MVC) aims to find the cluster structure shared by multiple views of a particular dataset. Existing MVC methods mainly integrate the raw data from different views, while ignoring the high-level information. Thus, their performance may degrade due to the conflict between heterogeneous features and the noises existing in each individual view. To overcome this problem, we propose a novel Multi-View Ensemble Clustering (MVEC) framework to solve MVC in an Ensemble Clustering (EC) way, which generates Basic Partitions (BPs) for each view individually and seeks for a consensus partition among all the BPs. By this means, we naturally leverage the complementary information of multi-view data in the same partition space. Instead of directly fusing BPs, we employ the low-rank and sparse decomposition to explicitly consider the connection between different views and detect the noises in each view. Moreover, the spectral ensemble clustering task is also involved by our framework with a carefully designed constraint, making MVEC a unified optimization framework to achieve the final consensus partition. Experimental results on six real-world datasets show the efficacy of our approach compared with both MVC and EC methods.
Locality Preserving Projections for Grassmann manifold
Authors: Wang Boyue , Hu Yongli , Gao Junbin , Sun Yanfeng , Chen Haoran , Ali Muhammad , Yin Baocai
Published: July 2017

Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show show its clear advantages over other Grassmann based algorithms.
Interactive Image Segmentation via Pairwise Likelihood Learning
Authors: Wang Tao , Sun Quansen , Ge Qi , Ji Zexuan , Chen Qiang , Xia Guiyu
Published: July 2017

This paper presents an interactive image segmentation approach where the segmentation problem is formulated as a probabilistic estimation manner. Instead of measuring the distances between unseeded pixels and seeded pixels, we measure the similarities between pixel pairs and seed pairs to improve the robustness to the seeds. The unary prior probability of each pixel belonging to the foreground F and background B can be effectively estimated based on the similarities with label pairs (F, F),(F, B),(B, F) and (B, B). Then a likelihood learning framework is proposed to fuse the region and boundary information of the image by imposing the smoothing constraint on the unary potentials. Experiments on challenging data sets demonstrate that the proposed method can obtain better performance than state-of-the-art methods.
Multiple Medoids based Multi-view Relational Fuzzy Clustering with Minimax Optimization
Authors: Wang Yangtao , Chen Lihui , Li Xiao-Li
Published: July 2017

Multi-view data becomes prevalent nowadays because more and more data can be collected from various sources. Each data set may be described by different set of features, hence forms a multi-view data set or multi-view data in short. To find the underlying pattern embedded in an unlabelled multi-view data, many multi-view clustering approaches have been proposed. Fuzzy clustering in which a data object can belong to several clusters with different memberships is widely used in many applications. However, in most of the fuzzy clustering approaches, a single center or medoid is considered as the representative of each cluster in the end of clustering process. This may not be sufficient to ensure accurate data analysis. In this paper, a new multi-view fuzzy clustering approach based on multiple medoids and minimax optimization called M4-FC for relational data is proposed. In M4-FC, every object is considered as a medoid candidate with a weight. The higher the weight is, the more likely the object is chosen as the final medoid. In the end of clustering process, there may be more than one mediod in each cluster. Moreover, minimax optimization is applied to find consensus clustering results of different views with its set of features. Extensive experimental studies on several multi-view data sets including real world image and document data sets demonstrate that M4-FC not only outperforms single medoid based multi-view fuzzy clustering approach, but also performs better than existing multi-view relational clustering approaches.
Multiple Kernel Clustering Framework with Improved Kernels
Authors: Wang Yueqing , Liu Xinwang , Dou Yong , Li Rongchun
Published: July 2017

Multiple kernel clustering (MKC) algorithms have been successfully applied into various applications. However, these successes are largely dependent on the quality of pre-defined base kernels, which cannot be guaranteed in practical applications. This may adversely affect the clustering performance. To address this issue, we propose a simple while effective framework to adaptively improve the quality of these base kernels. Under our framework, we instantiate three MKC algorithms based on the widely used multiple kernel $k$-means clustering (MKKM), MKKM with matrix-induced regularization (MKKM-MR) and co-regularized multi-view spectral clustering (CRSC). After that, we design the corresponding algorithms with proved convergence to solve the resultant optimization problems. To the best of our knowledge, our framework fills the gap between kernel adaption and clustering procedure for the first time in the literature and is readily extendable. Extensive experimental research has been conducted on 7 MKC benchmarks. As is shown, our algorithms consistently and significantly improve the performance of the base MKC algorithms, indicating the effectiveness of the proposed framework. Meanwhile, our framework shows better performance than compared ones with imperfect kernels.
Doubly Sparsifying Network
Authors: Wang Zhangyang , Huang Shuai , Zhou Jiayu , Huang Thomas S.
Published: July 2017

We propose the doubly sparsifying network (DSN), by drawing inspirations from the double sparsity model for dictionary learning. DSN emphasizes the joint utilization of both the problem structure and the parameter structure. It simultaneously sparsifies the output features and the learned model parameters, under one unified framework. DSN enjoys intuitive model interpretation, compact model size and low complexity. We compare DSN against a few carefully-designed baselines, to verify its consistently superior performance in a wide range of settings. Encouraged by its robustness to insufficient training data, we explore the applicability of DSN in brain signal processing that has been a challenging interdisciplinary area. DSN is evaluated for two mainstream tasks, electroencephalographic (EEG) signal classification and blood oxygenation level dependent (BOLD) response prediction, both achieving promising results.
Improving Reinforcement Learning with Confidence-Based Demonstrations
Authors: Wang Zhaodong , Taylor Matthew E.
Published: July 2017

Reinforcement learning has had many successes, but in practice it often requires significant amounts of data to learn high-performing policies. One common way to improve learning is to allow a trained (source) agent to assist a new (target) agent. The goals in this setting are to 1) improve the target agent's performance, relative to learning unaided, and 2) allow the target agent to outperform the source agent. Our approach leverages source agent demonstrations, removing any requirements on the source agent's learning algorithm or representation. The target agent then estimates the source agent's policy and improves upon it. The key contribution of this work is to show that leveraging the target agent's uncertainty in the source agent's policy can significantly improve learning in two complex simulated domains, Keepaway and Mario.
Learning from Demonstrations with High-Level Side Information
Authors: Wen Min , Papusha Ivan , Topcu Ufuk
Published: July 2017

We consider the problem of learning from demonstration, where extra side information about the demonstration is encoded as a co-safe linear temporal logic formula. We address two known limitations of existing methods that do not account for such side information. First, the policies that result from existing methods, while matching the expected features or likelihood of the demonstrations, may still be in conflict with high-level objectives not explicit in the demonstration trajectories. Second, existing methods fail to provide a priori guarantees on the out-of-sample generalization performance with respect to such high-level goals. This lack of formal guarantees can prevent the application of learning from demonstration to safety-

critical systems, especially when inference to state space regions with poor demonstration coverage is required. In this work, we show that side information, when explicitly taken into account, indeed improves the performance and safety of the learned policy with respect to task implementation. Moreover, we describe an automated procedure to systematically generate the features that encode side information expressed in temporal logic.
Contest Design with Uncertain Performance and Costly Participation
Authors: Levy Priel , Sarne David , Rochlin Igor
Published: July 2017

This paper studies the problem of designing contests for settings where a principal seeks to optimize the quality of the best performance obtained, and potential contestants only strategize about whether to participate in the contest, as participation incurs some cost. This type of contest can be mapped to various real-life settings (e.g., an audition, a beauty pageant, technology crowdsourcing). The paper provides a comparative game-theoretic based solution to two variants of the above underlying model: parallel and sequential contest, enabling a characterization of the equilibrium strategies in each. Special emphasis is placed on the case where the contestants are homogeneous which is often the case in real-life whenever the contestants are basically alike and their ranking in the contest is mostly influenced by some probabilistic factors (e.g., luck). Here, several (somehow counter-intuitive) properties of the equilibrium are proved, in particular for the sequential contest, leading to a comprehensive characterization of the principal preference between the two.
Linear Manifold Regularization with Adaptive Graph for Semi-supervised Dimensionality Reduction
Authors: Xiong Kai , Nie Feiping , Han Junwei
Published: July 2017

Many previous graph-based methods perform dimensionality reduction on a pre-defined graph. However, due to the noise and redundant information in the original data, the pre-defined graph has no clear structure and may not be appropriate for the subsequent task. To overcome the drawbacks, in this paper, we propose a novel approach called linear manifold regularization with adaptive graph (LMRAG) for semi-supervised dimensionality reduction. LMRAG directly incorporates the graph construction into the objective function, thus the projection matrix and the optimal graph can be simultaneously optimized. Due to the structure constraint, the learned graph is sparse and has clear structure. Extensive experiments on several benchmark datasets demonstrate the effectiveness of the proposed method.
Incomplete Label Distribution Learning
Authors: Xu Miao , Zhou Zhi-Hua
Published: July 2017

Label distribution learning (LDL) assumes labels can be associated to an instance to some degree, thus it can learn the relevance of a label to a particular instance. Although LDL has got successful practical applications, one problem with existing LDL methods is that they are designed for data with \emph{complete} supervised information, while in reality, annotation information may be \emph{incomplete}, because assigning each label a real value to indicate its association with a particular instance will result in large cost in labor and time. In this paper, we will solve LDL problem when given \emph{incomplete} supervised information. We propose an objective based on trace norm minimization to exploit the correlation between labels. We develop a proximal gradient descend algorithm and an algorithm based on alternating direction method of multipliers. Experiments validate the effectiveness of our proposal.
FolkPopularityRank: Tag Recommendation for Enhancing Social Popularity using Text Tags in Content Sharing Services
Authors: Yamasaki Toshihiko , Hu Jiani , Sano Shumpei , Aizawa Kiyoharu
Published: July 2017

In this study, we address two emerging yet challenging problems in social media: (1) scoring the text tags in terms of the influence to the numbers of views, comments, and favorite ratings of images and videos on content sharing services, and (2) recommending additional tags to increase such popularity-related numbers.

For these purposes, we present the FolkPopularityRank algorithm, which can score text tags based on their ability to influence the popularity-related numbers.

The FolkPopularityRank algorithm is inspired by the PageRank and FolkRank algorithms but the scores of the tags are calculated not only by the co-occurrence of the tags but also by considering the popularity-related numbers of the content.

To the best of our knowledge, this is the first attempt to recommending tags that can enhance popularity attributes of social media.

We conducted extensive experiments with about 1,000 images.

We uploaded the photos with the recommended tags along with the original tags to Flickr as a real test, and obtained very promising results.
Semi-Supervised Deep Hashing with a Bipartite Graph
Authors: Yan Xinyu , Zhang Lijun , Li Wu-Jun
Published: July 2017

Recently, deep learning has been successfully applied to the problem of hashing, yielding remarkable performance compared to traditional methods with hand-crafted features. However, most of existing deep hashing methods are designed for the supervised scenario and require a large number of labeled data. In this paper, we propose a novel semi-supervised hashing method for image retrieval, named Deep Hashing with a Bipartite Graph (DHBG), to simultaneously learn embeddings, features and hash codes. More specifically, we construct a bipartite graph to discover the underlying structure of data, based on which an embedding is generated for each instance. Then, we feed raw pixels as well as embeddings to a deep neural network, and concatenate the resulting features to determine the hash code. Compared to existing methods, DHBG is a universal framework that is able to utilize various types of graphs and losses. Furthermore, we propose an inductive variant of DHBG to support out-of-sample extensions. Experimental results on real datasets show that our DHBG outperforms state-of-the-art hashing methods.
Positive unlabeled learning via wrapper-based adaptive sampling
Authors: Yang Pengyi , Liu Wei , Yang Jean
Published: July 2017

Learning from positive and unlabeled data frequently occurs in applications where only a subset of positive instances is available while the rest of the data are unlabeled. In such scenarios, often the goal is to create a discriminant model that can accurately classify both positive and negative data by modelling from labeled and unlabeled instances. In this study, we propose an adaptive sampling (AdaSampling) approach that utilises prediction probabilities from a model to iteratively update the training data. Starting with equal prior probabilities for all unlabeled data, our method "wraps" around a predictive model to iteratively update these probabilities to distinguish positive and negative instances in unlabeled data. Subsequently, one or more robust negative set(s) can be drawn from unlabeled data, according to the likelihood of each instance being negative, to train a single classification model or ensemble of models.
Recognizing Top-Monotonic Preference Profiles in Polynomial Time
Authors: Magiera Krzysztof , Faliszewski Piotr
Published: July 2017

We provide the first polynomial-time algorithm for recognizing if a profile of (possibly weak) preference orders is top-monotonic. Top-monotonicity is a generalization of the notions of single-peakedness and single-crossingness, defined by Barbera and Moreno. Top-monotonic profiles always have weak Condorcet winners and satisfy a variant of the median voter theorem. Our algorithm proceeds by reducing the recognition problem to the SAT-2CNF problem.
Multi-Task Deep Reinforcement Learning for Continuous Action Control
Authors: Yang Zhaoyang , Merrick Kathryn , Abbass Hussein , Jin Lianwen
Published: July 2017

In this paper, we propose a deep reinforcement learning algorithm to learn multiple tasks concurrently. A new network architecture is proposed in the algorithm which reduces the number of parameters needed by more than 75% per task compared to typical single-task deep reinforcement learning algorithms. The proposed algorithm and network fuse images with sensor data and were tested with up to 12 movement-based control tasks on a simulated Pioneer 3AT robot equipped with a camera and range sensors. Results show that the proposed algorithm and network can learn skills that are as good as the skills learned by a comparable single-task learning algorithm. Results also show that learning performance is consistent even when the number of tasks and the number of constraints on the tasks increased.
Open Category Classification by Adversarial Sample Generation
Authors: Yu Yang , Qu Wei-Yang , Li Nan , Guo Zimin
Published: July 2017

In real-world classification tasks, it is difficult to collect training samples from all possible categories of the environment. Therefore, when an instance of an unseen class appears in the prediction stage, a robust classifier should be able to tell that it is from an unseen class, instead of classifying it to be any known category. In this paper, adopting the idea of adversarial learning, we propose the ASG framework for open-category classification. ASG generates positive and negative samples of seen categories in the unsupervised manner via an adversarial learning strategy. With the generated samples, ASG then learns to tell seen from unseen in the supervised manner. Experiments performed on several datasets show the effectiveness of ASG.
User Profile Preserving Social Network Embedding
Authors: Zhang Daokun , Yin Jie , Zhu Xingquan , Zhang Chengqi
Published: July 2017

This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Authors: Zhang Honglun , Xiao Liqiang , Wang Yongkun , Jin Yaohui
Published: July 2017

Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks. The architecture is structurally flexible and considers various interactions among tasks, which can be regarded as a generalized case of many previous works. Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others.