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1,553
Statistical Models for Co-occurrence Data
, 1998
"... Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from tw ..."
Abstract
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Cited by 97 (8 self)
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Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from
Euclidean embedding of co-occurrence data
- Advances in Neural Information Processing Systems 17
, 2005
"... Abstract Embedding algorithms search for low dimensional structure in complexdata, but most algorithms only handle objects of a single type for which pairwise distances are specified. This paper describes a method for em-bedding objects of different types, such as images and text, into a single comm ..."
Abstract
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Cited by 65 (1 self)
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common Euclidean space based on their co-occurrence statistics. Thejoint distributions are modeled as exponentials of Euclidean distances in the low-dimensional embedding space, which links the problem to con-vex optimization over positive semidefinite matrices. The local structure of our embedding
Word clustering and disambiguation based on co-occurrence data
- Natural Language Engineering
, 1998
"... We address the problem of clustering words (or con-structing a thesaurus) based on co-occurrence data, and using the acquired word classes to improve the accuracy of syntactic disambiguation. We view this problem as that of estimating a joint probability dis-tribution specifying the joint probabilit ..."
Abstract
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Cited by 61 (1 self)
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We address the problem of clustering words (or con-structing a thesaurus) based on co-occurrence data, and using the acquired word classes to improve the accuracy of syntactic disambiguation. We view this problem as that of estimating a joint probability dis-tribution specifying the joint
Clustering Large and Sparse Co-occurrence Data
, 2003
"... Abstract A novel approach to clustering co-occurrence data poses it as an optimization problem in information theory-- in this framework, an optimal clustering is one which minimizes the loss in mutual information. Recently a divisive clustering algorithm was proposed that monotonically reduces this ..."
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Abstract A novel approach to clustering co-occurrence data poses it as an optimization problem in information theory-- in this framework, an optimal clustering is one which minimizes the loss in mutual information. Recently a divisive clustering algorithm was proposed that monotonically reduces
Information Theoretic Clustering of Sparse Co-Occurrence Data
"... A novel approach to clustering co-occurrence data poses it as an optimization problem in information theory which minimizes the resulting loss in mutual information. A divisive clustering algorithm that monotonically reduces this loss function was recently proposed. In this paper we show that sparse ..."
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A novel approach to clustering co-occurrence data poses it as an optimization problem in information theory which minimizes the resulting loss in mutual information. A divisive clustering algorithm that monotonically reduces this loss function was recently proposed. In this paper we show
Information theoretic clustering of sparse co-occurrence data
- In Proceedings of the Third IEEE International Conference on Data Mining (ICDM-03
, 2003
"... ..."
Using Co-occurrence Data for Query Expansion: Wrong Paradigm or Wrong Formulas?
"... The paper discusses possible reasons for the failure of studies using co-occurrence data for query expansion. It suggests that the choice of similarity measures, the way expansion is done and the size of the corpus used to extract the co-occurrence data may be the reasons for this failure and not th ..."
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The paper discusses possible reasons for the failure of studies using co-occurrence data for query expansion. It suggests that the choice of similarity measures, the way expansion is done and the size of the corpus used to extract the co-occurrence data may be the reasons for this failure
Information bottleneck for non co-occurrence data
- In Advances in Neural Information Processing Systems 19
, 2007
"... We present a general model-independent approach to the analysis of data in cases when these data do not appear in the form of co-occurrence of two variables X, Y, but rather as a sample of values of an unknown (stochastic) function Z(X, Y). For example, in gene expression data, the expression level ..."
Abstract
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Cited by 12 (5 self)
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We present a general model-independent approach to the analysis of data in cases when these data do not appear in the form of co-occurrence of two variables X, Y, but rather as a sample of values of an unknown (stochastic) function Z(X, Y). For example, in gene expression data, the expression level
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data
"... We consider dynamic co-occurrence data, such as author-word links in papers published in successive years of the same conference. For static co-occurrence data, researchers often seek an embedding of the entities (authors and words) into a lowdimensional Euclidean space. We generalize a recent stati ..."
Abstract
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Cited by 16 (1 self)
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We consider dynamic co-occurrence data, such as author-word links in papers published in successive years of the same conference. For static co-occurrence data, researchers often seek an embedding of the entities (authors and words) into a lowdimensional Euclidean space. We generalize a recent
5O4 Hierarchical Latent Knowledge Analysis for Co-occurrence Data
"... Two-mode and co-occurrence data have been frequently seen in the real world. We address the issue of predicting unknown cooccurrence events from known events. For this issue, we propose a new method that naturally combines observable co-occurrence events with their existing latent knowledge by using ..."
Abstract
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Two-mode and co-occurrence data have been frequently seen in the real world. We address the issue of predicting unknown cooccurrence events from known events. For this issue, we propose a new method that naturally combines observable co-occurrence events with their existing latent knowledge
Results 1 - 10
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1,553
