OpenRaiser/Intern-Atlas huggingface dataset:
Mining discrete patterns via binary matrix factorization
Authors: Bao-Hong Shen, Shuiwang Ji, Jieping Ye
Venue: Conference KDD 2009
Year: 2009

Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rank-one binary matrix approximations that identify the dominant patterns of the data, while preserving its discrete property. A best approximation on such data has a minimum set of inconsistent entries, i.e., mismatches between the given binary data and the approximate matrix. Due to the hardness of the problem, previous accounts of such problems employ heuristics and the resulting approximation may be far away from the optimal one. In this paper, we show that the rank-one binary matrix approximation can be reformulated as a 0-1 integer linear program (ILP). However, the ILP formulation is computationally expensive even for small-size matrices. We propose a linear program (LP) relaxation, which is shown to achieve a guaranteed approximation error bound. We further extend the proposed formulations using the regularization technique, which is commonly employed to address overfitting. The LP formulation is restricted to medium-size matrices, due to the large number of variables involved for large matrices. Interestingly, we show that the proposed approximate formulation can be transformed into an instance of the minimum s-t cut problem, which can be solved efficiently by finding maximum flows. Our empirical study shows the efficiency of the proposed algorithm based on the maximum flow. Results also confirm the established theoretical bounds.
Entropy-based subspace clustering for mining numerical data
Authors: Chun Hung Cheng, Ada Wai-Chee Fu, Yi Zhang
Venue: Conference KDD 1999
Year: 1999

Mining numerical data is a relatively difficult problem in data mining. Clustering is one of the techniques. We consider a database with numerical attributes, in which each transaction is viewed as a multi-dimensional vector. By studying the clusters formed by these vectors, we can discover certain behaviors hidden in the data. Traditional clustering algorithms find clusters in the full space of the data sets. This results in high dimensional clusters, which are poorly comprehensible to human. One important task in this setting is the ability to discover clusters embedded in the subspaces of a high-dimensional data set. This problem is known as subspace clustering. We follow the basic assumptions of previous work CLIQUE. It is found that the number of subspaces with clustering is very large, and a criterion called the coverage is proposed in CLIQUE for the pruning. In addition to coverage, we identify new useful criteria for this problem and propose an entropybased algorithm called ENC...
Representing documents through their readers
Authors: Khalid El-Arini, Min Xu, Emily B. Fox, Carlos Guestrin
Venue: Conference KDD 2013
Year: 2013

From Twitter to Facebook to Reddit, users have become accustomed to sharing the articles they read with friends or followers on their social networks. While previous work has modeled what these shared stories say about the user who shares them, the converse question remains unexplored: what can we learn about an article from the identities of its likely readers? To address this question, we model the content of news articles and blog posts by attributes of the people who are likely to share them. For example, many Twitter users describe themselves in a short profile, labeling themselves with phrases such as "vegetarian" or "liberal." By assuming that a user's labels correspond to topics in the articles he shares, we can learn a labeled dictionary from a training corpus of articles shared on Twitter. Thereafter, we can code any new document as a sparse non-negative linear combination of user labels, where we encourage correlated labels to appear together in the output via a structured sparsity penalty. Finally, we show that our approach yields a novel document representation that can be effectively used in many problem settings, from recommendation to modeling news dynamics. For example, while the top politics stories will change drastically from one month to the next, the "politics" label will still be there to describe them. We evaluate our model on millions of tweeted news articles and blog posts collected between September 2010 and September 2012, demonstrating that our approach is effective.
One theme in all views: modeling consensus topics in multiple contexts
Authors: Jian Tang, Ming Zhang, Qiaozhu Mei
Venue: Conference KDD 2013
Year: 2013

New challenges have been presented to classical topic models when applied to social media, as user-generated content suffers from significant problems of data sparseness. A variety of heuristic adjustments to these models have been proposed, many of which are based on the use of context information to improve the performance of topic modeling. Existing contextualized topic models rely on arbitrary manipulation of the model structure, by incorporating various context variables into the generative process of classical topic models in an ad hoc manner. Such manipulations usually result in much more complicated model structures, sophisticated inference procedures, and low generalizability to accommodate arbitrary types or combinations of contexts. In this paper we explore a different direction. We propose a general solution that is able to exploit multiple types of contexts without arbitrary manipulation of the structure of classical topic models. We formulate different types of contexts as multiple views of the partition of the corpus. A co-regularization framework is proposed to let these views collaborate with each other, vote for the consensus topics, and distinguish them from view-specific topics. Experiments with real-world datasets prove that the proposed method is both effective and flexible to handle arbitrary types of contexts.
Retweet or not?: personalized tweet re-ranking
Authors: Wei Feng, Jianyong Wang
Venue: Conference WSDM 2013
Year: 2013

With Twitter being widely used around the world, users are facing enormous new tweets every day. Tweets are ranked in chronological order regardless of their potential interestedness. Users have to scan through pages of tweets to find useful information. Thus more personalized ranking scheme is needed to filter the overwhelmed information. Since retweet history reveals users' personal preference for tweets, we study how to learn a predictive model to rank the tweets according to their probability of being retweeted. In this way, users can find interesting tweets in a short time. To model the retweet behavior, we build a graph made up of three types of nodes: users, publishers and tweets. To incorporate all sources of information like users' profile, tweet quality, interaction history, etc, nodes and edges are represented by feature vectors. All these feature vectors are mapped to node weights and edge weights. Based on the graph, we propose a feature-aware factorization model to re-rank the tweets, which unifies the linear discriminative model and the low-rank factorization model seamlessly. Finally, we conducted extensive experiments on a real dataset crawled from Twitter. Experimental results show the effectiveness of our model.
Who will retweet me?: Finding retweeters in twitter
Authors: Zhunchen Luo, Miles Osborne, Jintao Tang, Ting Wang
Venue: Conference SIGIR 2013
Year: 2013

An important aspect of communication in Twitter (and other Social Network is message propagation -- people creating posts for others to share. Although there has been work on modelling how tweets in Twitter are propagated (retweeted), an untackled problem has been who will retweet a message. Here we consider the task of finding who will retweet a message posted on Twitter. Within a learning to-rank framework, we explore a wide range of features, such as retweet history, followers status, followers active time and followers interests. We find that followers who retweeted or mentioned the author's tweets frequently before and have common interests are more likely to be retweeters.
Predicting user interests from contextual information
Authors: Ryen W. White, Peter Bailey, Liwei Chen
Venue: Conference SIGIR 2009
Year: 2009

Search and recommendation systems must include contextual information to effectively model users' interests. In this paper, we present a systematic study of the effectiveness of five variant sources of contextual information for user interest modeling. Post-query navigation and general browsing behaviors far outweigh direct search engine interaction as an information-gathering activity. Therefore we conducted this study with a focus on Website recommendations rather than search results. The five contextual information sources used are: social, historic, task, collection, and user interaction. We evaluate the utility of these sources, and overlaps between them, based on how effectively they predict users' future interests. Our findings demonstrate that the sources perform differently depending on the duration of the time window used for future prediction, and that context overlap outperforms any isolated source. Designers of Website suggestion systems can use our findings to provide improved support for post-query navigation and general browsing behaviors.
Learning shift-invariant sparse representation of actions
Authors: Yi Li, Cornelia Fermüller, Yiannis Aloimonos, Hui Ji 0002
Venue: Conference CVPR 2010
Year: 2010

A central problem in the analysis of motion capture (MoCap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should facilitate the recognition, synthesis, and characterization of actions. We propose an unsupervised learning algorithm for automatically decomposing joint movements in human motion capture (MoCap) sequences into shift-invariant basis functions. Our formulation models the time series data of joint movements in actions as a sparse linear combination of short basis functions (snippets), which are executed (or “activated”) at different positions in time. Given a set of MoCap sequences of different actions, our algorithm finds the decomposition of MoCap sequences in terms of basis functions and their activations in time. Using the tools of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> minimization, the procedure alternately solves two large convex minimizations: Given the basis functions, a variant of Orthogonal Matching Pursuit solves for the activations, and given the activations, the Split Bregman Algorithm solves for the basis functions. Experiments demonstrate the power of the decomposition in a number of applications, including action recognition, retrieval, MoCap data compression, and as a tool for classification in the diagnosis of Parkinson (a motion disorder disease).
Sentribute: image sentiment analysis from a mid-level perspective
Authors: Jianbo Yuan, Sean Mcdonough, Quanzeng You, Jiebo Luo
Venue: Conference KDD 2013
Year: 2013

Visual content analysis has always been important yet challenging. Thanks to the popularity of social networks, images become an convenient carrier for information diffusion among online users. To understand the diffusion patterns and different aspects of the social images, we need to interpret the images first. Similar to textual content, images also carry different levels of sentiment to their viewers. However, different from text, where sentiment analysis can use easily accessible semantic and context information, how to extract and interpret the sentiment of an image remains quite challenging. In this paper, we propose an image sentiment prediction framework, which leverages the mid-level attributes of an image to predict its sentiment. This makes the sentiment classification results more interpretable than directly using the low-level features of an image. To obtain a better performance on images containing faces, we introduce eigenface-based facial expression detection as an additional mid-level attributes. An empirical study of the proposed framework shows improved performance in terms of prediction accuracy. More importantly, by inspecting the prediction results, we are able to discover interesting relationships between mid-level attribute and image sentiment.
An hog-lbp human detector with partial occlusion handling
Authors: Xiaoyu Wang 0002, Tony X. Han, Shuicheng Yan
Venue: Conference ICCV 2009
Year: 2009

By combining Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) as the feature set, we propose a novel human detection approach capable of handling partial occlusion. Two kinds of detectors, i.e., global detector for whole scanning windows and part detectors for local regions, are learned from the training data using linear SVM. For each ambiguous scanning window, we construct an occlusion likelihood map by using the response of each block of the HOG feature to the global detector. The occlusion likelihood map is then segmented by Mean-shift approach. The segmented portion of the window with a majority of negative response is inferred as an occluded region. If partial occlusion is indicated with high likelihood in a certain scanning window, part detectors are applied on the unoccluded regions to achieve the final classification on the current scanning window. With the help of the augmented HOG-LBP feature and the global-part occlusion handling method, we achieve a detection rate of 91.3% with FPPW= 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> , 94.7% with FPPW= 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> , and 97.9% with FPPW= 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> on the INRIA dataset, which, to our best knowledge, is the best human detection performance on the INRIA dataset. The global-part occlusion handling method is further validated using synthesized occlusion data constructed from the INRIA and Pascal dataset.
Probabilistic question recommendation for question answering communities
Authors: Mingcheng Qu, Guang Qiu, Xiaofei He, Cheng Zhang, Hao Wu, Jiajun Bu, Chun Chen
Venue: Conference WWW 2009
Year: 2009

User-Interactive Question Answering (QA) communities such as Yahoo! Answers are growing in popularity. However, as these QA sites always have thousands of new questions posted daily, it is difficult for users to find the questions that are of interest to them. Consequently, this may delay the answering of the new questions. This gives rise to question recommendation techniques that help users locate interesting questions. In this paper, we adopt the Probabilistic Latent Semantic Analysis (PLSA) model for question recommendation and propose a novel metric to evaluate the performance of our approach. The experimental results show our recommendation approach is effective.
l2,1 regularized correntropy for robust feature selection
Authors: Ran He 0001, Tieniu Tan, Liang Wang 0001, Wei-Shi Zheng 0001
Venue: Conference CVPR 2012
Year: 2012

In this paper, we study the problem of robust feature extraction based on l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularized correntropy in both theoretical and algorithmic manner. In theoretical part, we point out that an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates convergence study and algorithmic development. In particular, a general formulation is accordingly proposed to unify l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization within a common framework. In algorithmic part, we propose an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularized correntropy algorithm to extract informative features meanwhile to remove outliers from training data. A new alternate minimization algorithm is also developed to optimize the non-convex correntropy objective. In terms of face recognition, we apply the proposed method to obtain an appearance-based model, called Sparse-Fisherfaces. Extensive experiments show that our method can select robust and sparse features, and outperforms several state-of-the-art subspace methods on largescale and open face recognition datasets.
Drosophila gene expression pattern annotation using sparse features and term-term interactions
Authors: Shuiwang Ji, Lei Yuan, Ying-Xin Li, Zhi-Hua Zhou, Sudhir Kumar, Jieping Ye
Venue: Conference KDD 2009
Year: 2009

The Drosophila gene expression pattern images document the spatial and temporal dynamics of gene expression and they are valuable tools for explicating the gene functions, interaction, and networks during Drosophila embryogenesis. To provide text-based pattern searching, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with ontology terms manually by human curators. We present a systematic approach for automating this task, because the number of images needing text descriptions is now rapidly increasing. We consider both improved feature representation and novel learning formulation to boost the annotation performance. For feature representation, we adapt the bag-of-words scheme commonly used in visual recognition problems so that the image group information in the BDGP study is retained. Moreover, images from multiple views can be integrated naturally in this representation. To reduce the quantization error caused by the bag-of-words representation, we propose an improved feature representation scheme based on the sparse learning technique. In the design of learning formulation, we propose a local regularization framework that can incorporate the correlations among terms explicitly. We further show that the resulting optimization problem admits an analytical solution. Experimental results show that the representation based on sparse learning outperforms the bag-of-words representation significantly. Results also show that incorporation of the term-term correlations improves the annotation performance consistently.
Strong supervision from weak annotation: Interactive training of deformable part models
Authors: Steve Branson, Pietro Perona, Serge J. Belongie
Venue: Conference ICCV 2011
Year: 2011

We propose a framework for large scale learning and annotation of structured models. The system interleaves interactive labeling (where the current model is used to semi-automate the labeling of a new example) and online learning (where a newly labeled example is used to update the current model parameters). This framework is scalable to large datasets and complex image models and is shown to have excellent theoretical and practical properties in terms of train time, optimality guarantees, and bounds on the amount of annotation effort per image. We apply this framework to part-based detection, and introduce a novel algorithm for interactive labeling of deformable part models. The labeling tool updates and displays in real-time the maximum likelihood location of all parts as the user clicks and drags the location of one or more parts. We demonstrate that the system can be used to efficiently and robustly train part and pose detectors on the CUB Birds-200-a challenging dataset of birds in unconstrained pose and environment.
Adaptive pose priors for pictorial structures
Authors: Benjamin Sapp, Chris Jordan, Ben Taskar
Venue: Conference CVPR 2010
Year: 2010

Pictorial structure (PS) models are extensively used for part-based recognition of scenes, people, animals and multi-part objects. To achieve tractability, the structure and parameterization of the model is often restricted, for example, by assuming tree dependency structure and unimodal, data-independent pairwise interactions. These expressivity restrictions fail to capture important patterns in the data. On the other hand, local methods such as nearest-neighbor classification and kernel density estimation provide non-parametric flexibility but require large amounts of data to generalize well. We propose a simple semi-parametric approach that combines the tractability of pictorial structure inference with the flexibility of non-parametric methods by expressing a subset of model parameters as kernel regression estimates from a learned sparse set of exemplars. This yields query-specific, image-dependent pose priors. We develop an effective shape-based kernel for upper-body pose similarity and propose a leave-one-out loss function for learning a sparse subset of exemplars for kernel regression. We apply our techniques to two challenging datasets of human figure parsing and advance the state-of-the-art (from 80% to 86% on the Buffy dataset), while using only 15% of the training data as exemplars.
Understanding and identifying advocates for political campaigns on social media
Authors: Suhas Ranganath, Xia Hu, Jiliang Tang, Huan Liu
Venue: Conference WSDM 2016
Year: 2016

Social media is increasingly being used to access and disseminate information on sociopolitical issues like gun rights and general elections. The popularity and openness of social media makes it conducive for some individuals, known as advocates, who use social media to push their agendas on these issues strategically. Identifying these advocates will caution social media users before reading their information and also enable campaign managers to identify advocates for their digital political campaigns. A significant challenge in identifying advocates is that they employ nuanced strategies to shape user opinion and increase the spread of their messages, making it difficult to distinguish them from random users posting on the campaign. In this paper, we draw from social movement theories and design a quantitative framework to study the nuanced message strategies, propagation strategies, and community structure adopted by advocates for political campaigns in social media. Based on observations of their social media activities manifesting from these strategies, we investigate how to model these strategies for identifying them. We evaluate the framework using two datasets from Twitter, and our experiments demonstrate its effectiveness in identifying advocates for political campaigns with ramifications of this work directed towards assisting users as they navigate through social media spaces.