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Information-theoretic metric learning
tl;dr
We present an information-theoretic approach to learning a Mahalanobis distance function that can handle a wide variety of constraints and can optionally incorporate a prior on the distance function. Expand
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Co-clustering documents and words using bipartite spectral graph partitioning
tl;dr
We present a spectral co-clustering algorithm that uses the second left and right singular vectors of an appropriately scaled word-document matrix to solve the bipartite graph partitioning problem. Expand
  • 1,635
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Clustering with Bregman Divergences
tl;dr
In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences. Expand
  • 1,389
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Information-theoretic co-clustering
tl;dr
We present an innovative algorithm that monotonically increases the preserved mutual information by intertwining both the row and column clusterings at all stages. Expand
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Concept Decompositions for Large Sparse Text Data Using Clustering
tl;dr
We study a spherical k-means algorithm for clustering document vectors and show that, owing to the high-dimensionality and sparsity of the text data, the clusters produced by the algorithm have a certain "fractal-like" and "self-similar" behavior. Expand
  • 1,301
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Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
tl;dr
This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher distribution, which arises naturally for data distributed on the unit hypersphere. Expand
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Weighted Graph Cuts without Eigenvectors A Multilevel Approach
tl;dr
We discuss an equivalence between the objective functions used in these seemingly different methods - in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective. Expand
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Kernel k-means: spectral clustering and normalized cuts
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We show the generality of the weighted kernel k-means objective function, and derive the spectral clustering objective of normalized cut as a special case. Expand
  • 1,048
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Large-scale Multi-label Learning with Missing Labels
tl;dr
The multi-label classification problem has generated significant interest in recent years. Expand
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Guaranteed Rank Minimization via Singular Value Projection
tl;dr
In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization under affine constraints (ARMP) and show that SVP recovers the minimum rank solution for affine constraint that satisfy a restricted isometry property (RIP). Expand
  • 432
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