OpenRaiser/Intern-Atlas huggingface dataset:
Feature grouping and selection over an undirected graph
Authors: Sen Yang, Lei Yuan, Ying-Cheng Lai, Xiaotong Shen, Peter Wonka, Jieping Ye
Venue: Conference KDD 2012
Year: 2012

High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise <i>l</i><sub>∞</sub> norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated <i>l</i><sub>1</sub> regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.
A multi-task learning formulation for predicting disease progression
Authors: Jiayu Zhou, Lei Yuan, Jun Liu, Jieping Ye
Venue: Conference KDD 2011
Year: 2011

Alzheimer's Disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Identifying markers that can track the progress of the disease has recently received increasing attentions in AD research. A definitive diagnosis of AD requires autopsy confirmation, thus many clinical/cognitive measures including Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog) have been designed to evaluate the cognitive status of the patients and used as important criteria for clinical diagnosis of probable AD. In this paper, we propose a multi-task learning formulation for predicting the disease progression measured by the cognitive scores and selecting markers predictive of the progression. Specifically, we formulate the prediction problem as a multi-task regression problem by considering the prediction at each time point as a task. We capture the intrinsic relatedness among different tasks by a temporal group Lasso regularizer. The regularizer consists of two components including an L2,1-norm penalty on the regression weight vectors, which ensures that a small subset of features will be selected for the regression models at all time points, and a temporal smoothness term which ensures a small deviation between two regression models at successive time points. We have performed extensive evaluations using various types of data at the baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for predicting the future MMSE and ADAS-Cog scores. Our experimental studies demonstrate the effectiveness of the proposed algorithm for capturing the progression trend and the cross-sectional group differences of AD severity. Results also show that most markers selected by the proposed algorithm are consistent with findings from existing cross-sectional studies.
Model selection with the covering number of the ball of RKHS
Authors: Lizhong Ding, Shizhong Liao
Venue: Conference CIKM 2014
Year: 2014

Model selection in kernel methods is the problem of choosing an appropriate hypothesis space for kernel-based learning algorithms to avoid either underfitting or overfitting of the resulting hypothesis. One of main problems faced by model selection is how to control the sample complexity when designing the model selection criterion. In this paper, we take balls of reproducing kernel Hilbert spaces (RKHSs) as candidate hypothesis spaces and propose a novel model selection criterion via minimizing the empirical optimal error in the ball of RKHS and the covering number of the ball. By introducing the covering number to measure the capacity of the ball of RKHS, our criterion could directly control the sample complexity. Specifically, we first prove the relation between expected optimal error and empirical optimal error in the ball of RKHS. Using the relation as the theoretical foundation, we give the definition of our criterion. Then, by estimating the expectation of optimal empirical error and proving an upper bound of the covering number, we represent our criterion as a functional of the kernel matrix. An efficient algorithm is further developed for approximately calculating the functional so that the fast Fourier transform (FFT) can be applied to achieve a quasi-linear computational complexity. We also prove the consistency between the approximate criterion and the accurate one for large enough samples. Finally, we empirically evaluate the performance of our criterion and verify the consistency between the approximate and accurate criterion.
Eigenvalues perturbation of integral operator for kernel selection
Authors: Yong Liu, Shali Jiang, Shizhong Liao
Venue: Conference CIKM 2013
Year: 2013

Kernel selection is one of the key issues both in recent research and application of kernel methods. This is usually done by minimizing either an estimate of generalization error or some other related performance measure. It is well known that a kernel matrix can be interpreted as an empirical version of a continuous integral operator, and its eigenvalues converge to the eigenvalues of integral operator. In this paper, we introduce new kernel selection criteria based on the eigenvalues perturbation of the integral operator. This perturbation quantifies the difference between the eigenvalues of the kernel matrix and those of the integral operator. We establish the connection between eigenvalues perturbation and generalization error. By minimizing the derived generalization error bounds, we propose the kernel selection criteria. Therefore the kernel chosen by our proposed criteria can guarantee good generalization performance. To compute the values of our criteria, we present a method to obtain the eigenvalues of integral operator via the Fourier transform. Experiments on benchmark datasets demonstrate that our kernel selection criteria are sound and effective.
Learning kernels with upper bounds of leave-one-out error
Authors: Yong Liu, Shizhong Liao, Yuexian Hou
Venue: Conference CIKM 2011
Year: 2011

We propose a new leaning method for Multiple Kernel Learning (MKL) based on the upper bounds of the leave-one-out error that is an almost unbiased estimate of the expected generalization error. Specifically, we first present two new formulations for MKL by minimizing the upper bounds of the leave-one-out error. Then, we compute the derivatives of these bounds and design an efficient iterative algorithm for solving these formulations. Experimental results show that the proposed method gives better accuracy results than that of both SVM with the uniform combination of basis kernels and other state-of-art kernel learning approaches.
Temporal Corpus Summarization Using Submodular Word Coverage
Authors: Ruben Sipos, Adith Swaminathan, Pannaga Shivaswamy, Thorsten Joachims
Venue: Conference CIKM 2012
Year: 2012

In many areas of life, we now have almost complete electronic archives reaching back for well over two decades. This includes, for example, the body of research papers in computer science, all news articles written in the US, and most people's personal email. However, we have only rather limited methods for analyzing and understanding these collections. While keyword-based retrieval systems allow efficient access to individual documents in archives, we still lack methods for understanding a corpus as a whole. In this paper, we explore methods that provide a temporal summary of such corpora in terms of landmark documents, authors, and topics. In particular, we explicitly model the temporal nature of influence between documents and re-interpret summarization as a coverage problem over words anchored in time. The resulting models provide monotone sub-modular objectives for computing informative and non-redundant summaries over time, which can be efficiently optimized with greedy algorithms. Our empirical study shows the effectiveness of our approach over several baselines.
A bayesian hierarchical model for learning natural scene categories
Authors: Li Fei-Fei 0001, Pietro Perona
Venue: Conference CVPR 2005
Year: 2005

We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.
Multi-target tracking by on-line learned discriminative appearance models
Authors: Cheng-Hao Kuo, Chang Huang, Ram Nevatia
Venue: Conference CVPR 2010
Year: 2010

We present an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene from a single camera. Although much progress has been made in developing methods for optimal data association, there has been comparatively less work on the appearance models, which are key elements for good performance. Many previous methods either use simple features such as color histograms, or focus on the discriminability between a target and the background which does not resolve ambiguities between the different targets. We propose an algorithm for learning a discriminative appearance model for different targets. Training samples are collected online from tracklets within a time sliding window based on some spatial-temporal constraints; this allows the models to adapt to target instances. Learning uses an Ad-aBoost algorithm that combines effective image descriptors and their corresponding similarity measurements. We term the learned models as OLDAMs. Our evaluations indicate that OLDAMs have significantly higher discrimination between different targets than conventional holistic color histograms, and when integrated into a hierarchical association framework, they help improve the tracking accuracy, particularly reducing the false alarms and identity switches.
Part-based multiple-person tracking with partial occlusion handling
Authors: Guang Shu, Afshin Dehghan, Omar Oreifej, Emily M. Hand, Mubarak Shah
Venue: Conference CVPR 2012
Year: 2012

Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. In this paper, we address such problems by proposing a robust part-based tracking-by-detection framework. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Our approach learns part-based person-specific SVM classifiers which capture the articulations of the human bodies in dynamically changing appearance and background. With the part-based model, our approach is able to handle partial occlusions in both the detection and the tracking stages. In the detection stage, we select the subset of parts which maximizes the probability of detection, which significantly improves the detection performance in crowded scenes. In the tracking stage, we dynamically handle occlusions by distributing the score of the learned person classifier among its corresponding parts, which allows us to detect and predict partial occlusions, and prevent the performance of the classifiers from being degraded. Extensive experiments using the proposed method on several challenging sequences demonstrate state-of-the-art performance in multiple-people tracking.
Tracking of multiple, partially occluded humans based on static body part detection
Authors: Bo Wu 0001, Ram Nevatia
Venue: Conference CVPR 2006
Year: 2006

Tracking of humans in videos is important for many applications. A major source of difficulty in performing this task is due to inter-human or scene occlusion. We present an approach based on representing humans as an assembly of four body parts and detection of the body parts in single frames which makes the method insensitive to camera motions. The responses of the body part detectors and a combined human detector provide the "observations" used for tracking. Trajectory initialization and termination are both fully automatic and rely on the confidences computed from the detection responses. An object is tracked by data association if its corresponding detection response can be found; otherwise it is tracked by a meanshift style tracker. Our method can track humans with both inter-object and scene occlusions. The system is evaluated on three sets of videos and compared with previous method.
Knowledge sharing and yahoo answers: everyone knows something
Authors: Lada A. Adamic, Jun Zhang, Eytan Bakshy, Mark S. Ackerman
Venue: Conference WWW 2008
Year: 2008

Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one's curiosity about a countless number of things. In this paper, we seek to understand YA's knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users' interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker.
Finding the right facts in the crowd: factoid question answering over social media
Authors: Jiang Bian, Yandong Liu, Eugene Agichtein, Hongyuan Zha
Venue: Conference WWW 2008
Year: 2008

Community Question Answering has emerged as a popular and effective paradigm for a wide range of information needs. For example, to find out an obscure piece of trivia, it is now possible and even very effective to post a question on a popular community QA site such as Yahoo! Answers, and to rely on other users to provide answers, often within minutes. The importance of such community QA sites is magnified as they create archives of millions of questions and hundreds of millions of answers, many of which are invaluable for the information needs of other searchers. However, to make this immense body of knowledge accessible, effective answer retrieval is required. In particular, as any user can contribute an answer to a question, the majority of the content reflects personal, often unsubstantiated opinions. A ranking that combines both relevance and quality is required to make such archives usable for factual information retrieval. This task is challenging, as the structure and the contents of community QA archives differ significantly from the web setting. To address this problem we present a general ranking framework for factual information retrieval from social media. Results of a large scale evaluation demonstrate that our method is highly effective at retrieving well-formed, factual answers to questions, as evaluated on a standard factoid QA benchmark. We also show that our learning framework can be tuned with the minimum of manual labeling. Finally, we provide result analysis to gain deeper understanding of which features are significant for social media search and retrieval. Our system can be used as a crucial building block for combining results from a variety of social media content with general web search results, and to better integrate social media content for effective information access.
Orthogonal nonnegative matrix t-factorizations for clustering
Authors: Chris H. Q. Ding, Tao Li, Wei Peng, Haesun Park
Venue: Conference KDD 2006
Year: 2006

Currently, most research on nonnegative matrix factorization (NMF)focus on 2-factor $X=FG^T$ factorization. We provide a systematicanalysis of 3-factor $X=FSG^T$ NMF. While it unconstrained 3-factor NMF is equivalent to it unconstrained 2-factor NMF, itconstrained 3-factor NMF brings new features to it constrained 2-factor NMF. We study the orthogonality constraint because it leadsto rigorous clustering interpretation. We provide new rules for updating $F,S, G$ and prove the convergenceof these algorithms. Experiments on 5 datasets and a real world casestudy are performed to show the capability of bi-orthogonal 3-factorNMF on simultaneously clustering rows and columns of the input datamatrix. We provide a new approach of evaluating the quality ofclustering on words using class aggregate distribution andmulti-peak distribution. We also provide an overview of various NMF extensions andexamine their relationships.
The anatomy of a large-scale social search engine
Authors: Damon Horowitz, Sepandar D. Kamvar
Venue: Conference WWW 2010
Year: 2010

We present Aardvark, a social search engine. With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user's extended social network most likely to be able to answer that question. As compared to a traditional web search engine, where the challenge lies in finding the right document to satisfy a user's information need, the challenge in a social search engine like Aardvark lies in finding the right person to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search engine like Aardvark, trust is based on intimacy. We describe how these considerations inform the architecture, algorithms, and user interface of Aardvark, and how they are reflected in the behavior of Aardvark users.
Identifying topical authorities in microblogs
Authors: Aditya Pal, Scott Counts
Venue: Conference WSDM 2011
Year: 2011

Content in microblogging systems such as Twitter is produced by tens to hundreds of millions of users. This diversity is a notable strength, but also presents the challenge of finding the most interesting and authoritative authors for any given topic. To address this, we first propose a set of features for characterizing social media authors, including both nodal and topical metrics. We then show how probabilistic clustering over this feature space, followed by a within-cluster ranking procedure, can yield a final list of top authors for a given topic. We present results across several topics, along with results from a user study confirming that our method finds authors who are significantly more interesting and authoritative than those resulting from several baseline conditions. Additionally our algorithm is computationally feasible in near real-time scenarios making it an attractive alternative for capturing the rapidly changing dynamics of microblogs.
Relational learning via latent social dimensions
Authors: Lei Tang, Huan Liu
Venue: Conference KDD 2009
Year: 2009

Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, relational learning has been proposed, and collective inference based on network connectivity is adopted for prediction. However, connections in social media are often multi-dimensional. An actor can connect to another actor for different reasons, e.g., alumni, colleagues, living in the same city, sharing similar interests, etc. Collective inference normally does not differentiate these connections. In this work, we propose to extract latent social dimensions based on network information, and then utilize them as features for discriminative learning. These social dimensions describe diverse affiliations of actors hidden in the network, and the discriminative learning can automatically determine which affiliations are better aligned with the class labels. Such a scheme is preferred when multiple diverse relations are associated with the same network. We conduct extensive experiments on social media data (one from a real-world blog site and the other from a popular content sharing site). Our model outperforms representative relational learning methods based on collective inference, especially when few labeled data are available. The sensitivity of this model and its connection to existing methods are also examined.
Wisdom in the Social Crowd: An Analysis of Quora
Authors: Gang Wang, Konark Gill, Manish Mohanlal, Haitao Zheng, Ben Y. Zhao
Venue: Conference WWW 2013
Year: 2013

Efforts such as Wikipedia have shown the ability of user communities to collect, organize and curate information on the Internet. Recently, a number of question and answer (Q&A) sites have successfully built large growing knowledge repositories, each driven by a wide range of questions and answers from its users community. While sites like Yahoo Answers have stalled and begun to shrink, one site still going strong is Quora, a rapidly growing service that augments a regular Q&A system with social links between users. Despite its success, however, little is known about what drives Quora's growth, and how it continues to connect visitors and experts to the right questions as it grows.
CQArank: Jointly model topics and expertise in community question answering
Authors: Liu Yang, Minghui Qiu, Swapna Gottipati, Feida Zhu, Jing Jiang, Huiping Sun, Zhong Chen
Venue: Conference CIKM 2013
Year: 2013

Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. Experiments carried out on Stack Overflow data, the largest CQA focused on computer programming, show that our method achieves significant improvement over existing methods on multiple metrics.