点击次数：

论文类型：期刊论文

发表时间：2018-01-01

发表刊物：IEEE SIGNAL PROCESSING LETTERS

收录刊物：SCIE、EI、Scopus

卷号：25

期号：1

页面范围：60-64

ISSN号：1070-9908

关键字：Correlated feature learning; data clustering; multiview data; unsupervised learning

摘要：Multiview data, which provide complementary information for consensus grouping, are very common in real-world applications. However, synthesizing multiple heterogeneous features to learn a comprehensive description of the data samples is challenging. To tackle this problem, many methods explore the correlations among various features across different views by the assumption that all views share the common semantic information. Following this line, in this letter, we propose a new unsupervised multiview nonnegative correlated feature learning (UMCFL) method for data clustering. Different from the existing methods that only focus on projecting features from different views to a shared semantic subspace, ourmethod learns view-specific features and captures inter-view feature correlations in the latent common subspace simultaneously. By separating the view-specific features from the shared feature representation, the effect of the individual information of each view can be removed. Thus, UMCFL can capture flexible feature correlations hidden in multiview data. A new objective function is designed and efficient optimization processes are derived to solve the proposed UMCFL. Extensive experiments on real-world multiview datasets demonstrate that the proposed UMCFL method is superior to the state-of-the-art multiview clustering methods.