Multi-view Subspace Adaptive Learning via Autoencoder and Attention
This work addresses the challenge of effectively fusing multiple views in subspace clustering, which is incremental as it builds upon existing methods like MLRSSC.
The paper tackles the problem of multi-view subspace clustering by proposing MSALAA, which integrates a deep autoencoder with attention-based alignment of self-representations, achieving significant improvements over baseline methods on six real-world datasets.
Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clustering (LRSC), cluster the affinity matrix for a single view, thus ignoring the problem of fusion between views. In our article, we propose a new Multiview Subspace Adaptive Learning based on Attention and Autoencoder (MSALAA). This method combines a deep autoencoder and a method for aligning the self-representations of various views in Multi-view Low-Rank Sparse Subspace Clustering (MLRSSC), which can not only increase the capability to non-linearity fitting, but also can meets the principles of consistency and complementarity of multi-view learning. We empirically observe significant improvement over existing baseline methods on six real-life datasets.