Non-negative Tensor Factorization Based Bi-Clustering on Anchor Graphs.

Abstract

Non-negative matrix factorization (NMF) is a powerful technique for clustering analysis. Applying it to multi-view clustering, the existing methods typically apply NMF to obtain soft label matrices for each view independently and then fuse the obtained label matrices of views to generate view-consistent label matrix. A major drawback is that they mainly focus on decision-level fusion and can't effectively exploit the implicit relationships between views at the data level, which are critical for multi-view clustering. Moreover, they rely on the relationship between NMF and K-means to derive labels, which has weak interpretability. To address these problems, we propose a novel multi-view clustering model based on non-negative tensor factorization (NTF), which employs multi-level fusion (both data-level and decision-level) to achieve view-consistent labels for multi-view data. Specifically, we present a non-negative tensor factorization and utilize it to decompose tensorized anchor graph into the product of an anchor indicator tensor and a data indicator tensor from a probabilistically interpretable perspective, thereby enhancing the interpretability of our proposed model. Extensive experimental results demonstrate the effectiveness of our method.