Title: Unified Subspace Learning for Incomplete and Unlabeled Multi-View Data
Authors: Yin, QY; Wu, S; Wang, L
Author Full Names: Yin, Qiyue; Wu, Shu; Wang, Liang
Source: PATTERN RECOGNITION, 67 313-327; 10.1016/j.patcog.2017.01.035 JUL 2017
Language: English
Abstract: Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In this paper, we propose a novel subspace learning framework for incomplete and unlabeled multi-view data. The model directly optimizes the class indicator matrix, which establishes a bridge for incomplete feature sets. Besides, feature selection is considered to deal with high dimensional and noisy features. Furthermore, the inter-view and intra-view data similarities are preserved to enhance the model. To these ends, an objective is developed along with an efficient optimization strategy. Finally, extensive experiments are conducted for multi-view clustering and cross-modal retrieval, achieving the state-of-the-art performance under various settings. (C) 2017 Elsevier Ltd. All rights reserved.
ISSN: 0031-3203
eISSN: 1873-5142
IDS Number: ES4QV
Unique ID: WOS:000399520700026