Unsupervised feature analysis for high dimensional big data | Learning without teachers, an exploration of world in unsupervised data

Mingjie Qian

ISBN 10: 3659805157 / ISBN 13: 9783659805158

Published by LAP LAMBERT Academic Publishing, 2016

About this Item

Unsupervised feature analysis for high dimensional big data | Learning without teachers, an exploration of world in unsupervised data | Mingjie Qian | Taschenbuch | 144 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659805158 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 103936152

Bibliographic Details

• Title: Unsupervised feature analysis for high dimensional big data | Learning without teachers, an exploration of world in unsupervised data
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2016
• Language: English
• ISBN 10: 3659805157
• ISBN 13: 9783659805158
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 233 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.