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Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization

Published on Aug 26, 20135591 Views

Nicolas Gillis

Nonnegative matrix factorization (NMF) has been shown recently to be tractable under the separability assumption, which amounts for the columns of the input data matrix to belong to the convex cone

ROKS 2013 - Leuven

Related categories

Compressed SensingSupport Vector MachinesRegularizationOptimization Methods

Chapter list

Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization00:00
Nonnegative Matrix Factorization (NMF)00:08
Text Mining03:01
Hyperspectral Unmixing - 104:35
Hyperspectral Unmixing - 208:38
Hyperspectral Unmixing - 308:45
Can we only solve NMF problems?09:16
Separability Assumption10:42
Is separability a reasonable assumption?11:33
Geometric Interpretation of Separability13:24
Separable NMF14:34
Near-Separable NMF15:23
Near-Separable NMF: Noise and Conditioning - 115:52
Near-Separable NMF: Noise and Conditioning - 216:53
Hottopixx, a Linear Optimization Model - 116:58
An Improved Linear Optimization Model - 121:39
An Improved Linear Optimization Model - 222:03
Hottopixx, a Linear Optimization Model - 222:26
An Improved Linear Optimization Model - 322:32
An Improved Linear Optimization Model - 423:51
Numerical Experiments - 123:52
Numerical Experiments - 224:59
Numerical Experiments - 325:29
Conclusion25:42
Thank you27:04