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论文类型：期刊论文

发表时间：2021-01-10

发表刊物：IEEE SIGNAL PROCESSING LETTERS

卷号：27

页面范围：1750-1754

ISSN号：1070-9908

关键字：Optimization; Matrix decomposition; Learning systems; Clustering algorithms; Noise measurement; Aerospace electronics; Sun; Data Clustering; multi-view data; nonnegative matrix factorization; robust feature learning

摘要：Multi-view feature learning can provide basic information for consistent grouping, and is very common in practical applications, such as judicial document clustering. However, it is a challenge to combine multiple heterogeneous features to learn a comprehensive description of data samples. To solve this problem, many methods explore the correlation between various features across views by assuming that all views share the same semantic information. Inspired by this, in this paper we propose a new multi-view robust feature learning (MRFL) method. In addition to projecting features from different views to a shared semantic subspace, our approach also learns the irrelevant information of data space to capture the feature dependencies between views in potential common subspaces. Therefore, the MRFL can obtain flexible feature associations hidden in multi-view data. A new objective function is designed to derive, and solve the effective optimization process of MRFL. Experiments on real-world multi-view datasets show that the proposed MRFL method is superior to the state-of-the-art multi-view learning methods.