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Highly Cited

2010

Highly Cited

2010

The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of… Expand

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Highly Cited

2010

Highly Cited

2010

The restoration of blurred images corrupted by Poisson noise is an important task in various applications such as astronomical… Expand

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Highly Cited

2010

Highly Cited

2010

This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of… Expand

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Highly Cited

2009

Highly Cited

2009

In this paper, we study low-rank matrix nearness problems, with a focus on learning low-rank positive semidefinite (kernel… Expand

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Highly Cited

2009

Highly Cited

2009

A divergence measure between two probability distributions or positive arrays (positive measures) is a useful tool for solving… Expand

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Highly Cited

2007

Highly Cited

2007

This paper discusses a new class of matrix nearness problems that measure approximation error using a directed distance measure… Expand

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Highly Cited

2007

Highly Cited

2007

The Voronoi diagram of a finite set of objects is a fundamental geometric structure that subdivides the embedding space into… Expand

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Highly Cited

2005

Highly Cited

2005

A wide variety of distortion functions, such as squared Euclidean distance, Mahalanobis distance, Itakura-Saito distance and… Expand

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Highly Cited

2005

Highly Cited

2005

Nonnegative matrix approximation (NNMA) is a recent technique for dimensionality reduction and data analysis that yields a parts… Expand

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Highly Cited

2004

Highly Cited

2004

Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and… Expand

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