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Unsupervised feature analysis for high dimensional big data

Name: Unsupervised feature analysis for high dimensional big data
Brand: LAP Lambert Academic Publishing
SKU: 9783659805158
Price: 1219 CZK
Availability: InStock

Learning without teachers, an exploration of world in unsupervised data

Autor: Mingjie Qian

Jazyk:

Vazba: Brožovaná

Vydavatel: LAP Lambert Academic Publishing

Dostupnost: Skladem u dodavatele

Odesíláme za 5-8 dnů

1 219 Kč

For single-view unsupervised feature selection, we propose two novel methods RUFS and AUFS. RUFS con...

Informace o knize

Autor

Mingjie Qian

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2016

Stránek

144

EAN

9783659805158

Enbook ID

02953475

Vydavatel

LAP Lambert Academic Publishing

Hmotnost

233

Rozměry

150 x 220 x 9

Kompletní popis

For single-view unsupervised feature selection, we propose two novel methods RUFS and AUFS. RUFS considers outliers in both labeling learning and feature selection thus is more robust than state-of-the-arts. AUFS is proposed such that three desirable properties are satisfied: (1) Sparsity-inducing property; (2) Large weights and small weights are equally penalized; (3) Good balance between small loss on normal data examples and large loss on outliers. For multi-view unsupervised feature selection, we propose to directly utilize raw features in the main view to learn pseudo cluster labels which should also have the most consensus with other views, and meanwhile the discriminative features in the feature selection process will win out to contribute more on label learning process. For multi-view topic discovery, we propose a regularized nonnegative constrained $l_{2,1}$-norm minimization framework as a systematic solution that can integrate information propagation and mutual enhancement between data of different types without supervision in a principled way.