Beschreibung
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.
Autorenporträt
Dr. Qian received his bachelor's degree and master's degree in control science from Tsinghua University, and his Ph.D. degree in computer science from University of Illinois at Urbana-Champaign. He is a research scientist at Yahoo Labs at Sunnyvale, CA. He is an editorial board member of Information Processing and Management by Elsevier Science.
Herstellerkennzeichnung:
BoD - Books on Demand
In de Tarpen 42
22848 Norderstedt
DE
E-Mail: info@bod.de
