1394662
Dirichlet Enhanced Latent Semantic Analysis
This paper describes nonparametric Bayesian treatments for analyzing records containing occurrences of items. The introduced model retains the strength of previous approaches that explore the latent factors of each record (e.g. topics of documents), and further uncovers the clustering structure of records, which reflects the statistical dependencies of the latent factors. The nonparametric model induced by a Dirichlet process (DP) flexibly adapts model complexity to reveal the clustering structure of the data. To avoid the problems of dealing with infinite dimensions, we further replace the DP prior by a simpler alternative, namely Dirichlet-multinomial allocation (DMA), which maintains the main modelling properties of the DP. Instead of relying on Markov chain Monte Carlo (MCMC) for inference, this paper applies efficient variational inference based on DMA. The proposed approach yields encouraging empirical results on both a toy problem and text data. The results show that the proposed algorithm uncovers not only the latent factors, but also the clustering structure.
3164237
Multi-output regularized projection
Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs which is an area of growing interest. In this paper we introduce a novel projection framework which is sensitive to both input features and outputs. Based on the derived features prediction accuracy can be greatly improved. We validate our approach in two applications. The first is to model users' preferences on a set of paintings. The second application is concerned with image categorization where each image may belong to multiple categories. The proposed algorithm produces very encouraging results in both settings.
268086
Collaborative ordinal regression
Ordinal regression has become an effective way of learning user preferences, but most research focuses on single regression problems. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks are handled simultaneously. Rather than modeling each task individually, we explore the dependency between ranking functions through a hierarchical Bayesian model and assign a common Gaussian Process (GP) prior to all individual functions. Empirical studies show that our collaborative model outperforms the individual counterpart in preference learning applications.
11243052
Improving pseudo-relevance feedback in web information retrieval using web page segmentation
In contrast to traditional document retrieval, a web page as a whole is not a good information unit to search because it often contains multiple topics and a lot of irrelevant information from navigation, decoration, and interaction part of the page. In this paper, we propose a VIsion-based Page Segmentation (VIPS) algorithm to detect the semantic content structure in a web page. Compared with simple DOM based segmentation method, our page segmentation scheme utilizes useful visual cues to obtain a better partition of a page at the semantic level. By using our VIPS algorithm to assist the selection of query expansion terms in pseudo-relevance feedback in web information retrieval, we achieve 27% performance improvement on Web Track dataset.
10350219
Multi-label informed latent semantic indexing
Latent semantic indexing (LSI) is a well-known unsupervised approach for dimensionality reduction in information retrieval. However if the output information (i.e. category labels) is available, it is often beneficial to derive the indexing not only based on the inputs but also on the target values in the training data set. This is of particular importance in applications with multiple labels, in which each document can belong to several categories simultaneously. In this paper we introduce the multi-label informed latent semantic indexing (MLSI) algorithm which preserves the information of inputs and meanwhile captures the correlations between the multiple outputs. The recovered "latent semantics" thus incorporate the human-annotated category information and can be used to greatly improve the prediction accuracy. Empirical study based on two data sets, Reuters-21578 and RCV1, demonstrates very encouraging results.
2852882
Hierarchy-regularized latent semantic indexing
Organizing textual documents into a hierarchical taxonomy is a common practice in knowledge management. Beside textual features, the hierarchical structure of directories reflect additional and important knowledge annotated by experts. It is generally desired to incorporate this information into text mining processes. In this paper, we propose hierarchy-regularized latent semantic indexing, which encodes the hierarchy into a similarity graph of documents and then formulates an optimization problem mapping each document into a low dimensional vector space. The new feature space preserves the intrinsic structure of the original taxonomy and thus provides a meaningful basis for various learning tasks like visualization and classification. Our approach employs the information about class proximity and class specificity, and can naturally cope with multi-labeled documents. Our empirical studies show very encouraging results on two real-world data sets, the new Reuters (RCVI) benchmark and the Swissprot protein database.
122735646
Segmentation using multiscale cues
Edges at multiple scales provide complementary grouping cues for image segmentation. These cues are reliable within different ranges. The larger the scale of an edge, the longer range the grouping cues it designates, and the greater impact it has on the final segmentation. A good segmentation respects grouping cues at each scale. These intuitions are formulated in a graph-theoretic framework where multiscale edges define pairwise pixel affinity at multiple grids, each captured in one graph. A novel criterion called average cuts of normalized affinity is proposed to evaluate a simultaneous segmentation through all these graphs. Its near-global optima can be solved efficiently. With a sparse yet complete characterization of pairwise pixel affinity, this graph-cuts approach leads to a hierarchy of coarse to fine segmentations that naturally take care of textured regions and weak contours.
2320487
On Synchronization Languages
New constructs for synchronization termed synchronization expressions (SEs) have been developed as high-level language constructs for parallel programming languages [8, 9]. Statements that are constrained by certain synchronization requirements are tagged, and synchronization requests are specified as expressions of statement tags. In this paper, we introduce a new family of languages named synchronization languages which we use to give a precise semantic description for SEs. Under this description, relations such as equivalence and inclusion between SEs can be easily understood and tested. In practice, it also provides us with a systematic way for the implementation as well as the simplification of SEs in parallel programming languages. We show that each synchronization language is closed under the following rewriting rules: (1) asbs → bsas, (2) atbt → btat, (3) asbt → btas, (4) atasbtbs → btbsatas and also h(atasbtbs) → h(btbsatas) for any morphism h that satisfies certain conditions which will be specified in the paper. We conjecture that closure under the above rewriting rules is a sufficient condition for a regular st-language to be a synchronization language. Several other properties of synchronization languages are also studied.
14165952
Efficient Night Gait Recognition Based on Template Matching
Gait is a useful biometric which can be used to recognize people at a distance when other biometrics are incapable. However, most work on gait recognition has been visible spectrum-oriented over the past decade, ignoring recognition at night which is in reality demand-imperative. This paper deals with the problem of night gait recognition via thermal infrared imagery. First of all, human detection is accomplished, based on the Gaussian mixture modeling of the background. Then, human silhouettes are extracted on the basis of preceding detection results. Moreover, a new gait representation called HTI is proposed to characterize gait signatures for recognition. An infrared night gait database was built to provide a foundation for night gait recognition. Experimental results on two gait datasets show the effectiveness of this method
26537173
PET Studies of the Effects of ECT on Cerebral Physiology
ECT is a remarkably effective treatment for major depressive disorder [MDD]. Neuroimaging studies of rCBF and rCMR are helpful in addressing mechanisms of therapeutic effects. We have reported that reductions in rCBF in prefrontal regions are associated with positive response to ECT[1], and that a course of bilateral ECT is associated with marked reduction in prefrontal rCMR[2]. We now report on a new study of patients receiving ECT for MDD. This study provides the first comparison of the relative acute effects of seizures on both rCBF and rCMR.
14436503
Implementing a Model Checker for LEGO
Interactive theorem proving provides a general approach to modelling and verification of both hardware and software systems but requires significant human efforts to deal with many tedious proofs. To be effectively used in practice, we need some automatic tools such as model checkers to deal with those tedious proofs. In this paper, we formalise a verification system of both CCS and an imperative language in the proof development system LEGO which can be used to verify both finite and infinite problems. Then a model checker, LegoMC, is implemented to generate LEGO proof terms for finite-state problems automatically. Therefore people can use LEGO to verify a general problem with some of its finite sub-problems verified by LegoMC. On the other hand, this integration extends the power of model checking to verify more complicated and infinite models as well.