Results 1 - 10 of 127

Information-Theoretic Co-Clustering

by Inderjit S. Dhillon, Subramanyam Mallela, Dharmendra S. Modha - In KDD , 2003
"... Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation views ..."
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Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation

Abstract: A COMPARATIVE STUDY OF SPECTRAL CLUSTERING AND INFORMATION-THEORETIC CO-CLUSTERING FOR VIDEO SHOT CATEGORIZATION

by Peng Wang, Zhi-qiang Liu, Shi-qiang Yang
"... Automatic categorization of video shots is important in video indexing and retrieval. To improve the effectiveness of video shot categorization, current researchers have addressed two major issues: i) spatio-temporal coherence from shot to shot, and ii) bipartite correlation between descriptive feat ..."
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features and shot categories. In recent works, spectral clustering and information-theoretic co-clustering have been actively studied and used to solve the two issues, respectively. In this paper, we show a performance comparison between the two algorithms for video shot classification. The comparison

UNITY IN DIVERSITY: DISCOVERING TOPICS FROM WORDS Information Theoretic Co-clustering for Visual Categorization

by Ashish Gupta, Richard Bowden
"... This paper presents a novel approach to learning a codebook for visual categorization, that resolves the key issue of intra-category appearance variation found in complex real world datasets. The codebook of visual-topics (semantically equivalent descriptors) is made by grouping visual-words (syntac ..."
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-words (syntactically equivalent descriptors) that are scattered in feature space. We analyze the joint distribution of images and visual-words using information theoretic co-clustering to discover visual-topics. Our approach is compared with the standard ‘Bagof-Words’ approach. The statistically significant

Unsupervised auditory scene categorization via key audio effects and information-theoretic co-clustering

by Rui Cai, Lian-hong Cai - In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP05 , 2005
"... Automatic categorization of auditory scenes is very useful in various content-based multimedia applications, such as video indexing and context-aware computing. In this paper, an unsupervised approach is proposed to group auditory scenes with similar semantics. In our approach, auditory scenes are d ..."
Abstract - Cited by 6 (1 self) - Add to MetaCart
, Bayesian Information Criterion (BIC) is used to automatically select the cluster numbers for both the key effects and the auditory scenes. Evaluation on 272 auditory scenes extracted from 12-hour audio data shows very encouraging results. 1.

Regularized Co-Clustering on Manifold

by Ying Liu, Henry Han, Chengcheng Shen
"... Abstract—Co-clustering is to partition rows and columns of a matrix simultaneously. It has been an important research field in data mining and machine learning. It is preferred over traditional homogeneous clustering techniques in many real applications. In this paper, we present a co-clustering alg ..."
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. The experimental results show that the algorithm outperforms the existing spectral and information-theoretic co-clustering algorithms. The results also show that the algorithm correctly coclusters documents with related words. Keywords-co-clustering, data mining, machine learning, algorithm I.

Constrained Co-Clustering for Textual Documents

by Yangqiu Song, Shimei Pan, Shixia Liu, Furu Wei, Michelle X. Zhou, Weihong Qian - Proc. Conf. Artificial Intelligence (AAAI , 2010
"... In this paper, we present a constrained co-clustering approach for clustering textual documents. Our approach combines the benefits of information-theoretic co-clustering and constrained clustering. We use a two-sided hidden Markov random field (HMRF) to model both the document and word constraints. ..."
Abstract - Cited by 11 (5 self) - Add to MetaCart
In this paper, we present a constrained co-clustering approach for clustering textual documents. Our approach combines the benefits of information-theoretic co-clustering and constrained clustering. We use a two-sided hidden Markov random field (HMRF) to model both the document and word constraints

Fast Information-Theoretic Agglomerative Co-clustering

by Tiantian Gao, Leman Akoglu
"... Abstract. Jointly clustering the rows and the columns of large matrices, a.k.a. co-clustering, finds numerous applications in the real world such as collaborative filtering, market-basket and micro-array data analysis, graph clustering, etc. In this paper, we formulate an informationtheoretic object ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract. Jointly clustering the rows and the columns of large matrices, a.k.a. co-clustering, finds numerous applications in the real world such as collaborative filtering, market-basket and micro-array data analysis, graph clustering, etc. In this paper, we formulate an informationtheoretic

A MARKOV CLUSTERING METHOD FOR ANALYZING MOVEMENT TRAJECTORIES

by Jacob Goldberger, Keren Erez, Moshe Abeles
"... In this study we analyze monkeys ’ hand movement; our strategy is compositional, division of complex movement into basic simple components-primitives. Representing each trajectory segment as vectors of directions, we model the movement trajectory as a large Markov process where each state is related ..."
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is related with an average trajectory pattern. In the next step, in order to find the movements primitives, we cluster the Markov states according to their probabilistic similarity. We present an information theoretic co-clustering algorithm which can be interpreted as a block-matrix approximation

Co-ClusterD: A Distributed Framework for Data Co-Clustering with Sequential Updates

by Xiang Cheng , Sen Su , Fellow, IEEE Lixin Gao , Jiangtao Yin
"... Abstract-Co-clustering has emerged to be a powerful data mining tool for two-dimensional co-occurrence and dyadic data. However, co-clustering algorithms often require significant computational resources and have been dismissed as impractical for large data sets. Existing studies have provided stro ..."
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matrix tri-factorization (FNMTF) and information theoretic co-clustering (ITCC). We evaluate our framework on both a local cluster of machines and the Amazon EC2 cloud. Empirical results show that AMCC algorithms implemented in Co-ClusterD can achieve a much faster convergence and often obtain better

Unsupervised content discovery in composite audio

by Rui Cai, Alan Hanjalic - in Proc. ACM Multimedia, 2005
"... Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. Motivated by the known limitations of traditional supervised approaches to content extraction, which are hard to generalize and require suitable training data, we propose in this paper an uns ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
scenes are categorized in terms of the audio elements appearing therein. Categorization is inferred from the relations between audio elements and auditory scenes by using the information-theoretic co-clustering scheme. Evaluations of the proposed approach performed on 4 hours of diverse audio data
Results 1 - 10 of 127