Könyv Unsupervised feature analysis for high dimensional big data Mingjie Qian

Unsupervised feature analysis for high dimensional big data

Learning without teachers, an exploration of world in unsupervised data

Szerző: Mingjie Qian
Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Kiadói készleten rendelésre
Küldés 17-27 napon belül
21 412 Ft
For single-view unsupervised feature selection, we propose two novel methods RUFS and AUFS. RUFS con...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2016
oldal
144
EAN
9783659805158
Enbook ID
02953475
Súly
233
Méretek
150 x 220 x 9

Teljes leírás

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.

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