Consistent graph learning for multi-view spectral clustering

doi:10.1016/j.patcog.2024.110598

Consistent graph learning for multi-view spectral clustering

2024-10-01

1

PRE

AI

DOI：10.1016/j.patcog.2024.110598

Original

Original request for help

Save

Abstract

En 中文

Given the heterogeneous information of multiple views and the possible noise embedded in multi -view data, it is difficult to directly learn a consistent representation from multiple graphs to depict the intrinsic structure of all views. We propose a consistent graph learning method by exploiting both the high -order correlations underlying multiple views and the global structure of each single view. First, we calculate a transition probability matrix from each view. Second, a tensor is constructed by stacking each transition matrix as its frontal slice and then decomposed into the latent and error tensors. For the latent tensor, after rotated, a weighted tensor nuclear norm is used to encourage the rotate tensor to fully exploit the high -order correlations underlying multiple views. Furthermore, each frontal slice of the latent tensor is regularized by the nuclear norm to capture the global structure of each single view, and is restricted by probability constraints. Besides, we adopt the Frobenius-norm-based regularization to directly learn a common affinity matrix from the latent tensor. The established model is readily optimized by the alternating Lagrangian method. Extensive experiments on six real world datasets demonstrate that our method outperforms state-of-the-art methods.

Keywords:

Spectral clustering

Multi-view clustering

Tensor learning

Graph learning

Consistent graph learning for multi-view spectral clustering

Abstract

Journal

Organization