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22 March 2025 |
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Article overview
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Multi-view Subspace Adaptive Learning via Autoencoder and Attention | | Jian-wei Liu
; Hao-jie Xie
; Run-kun Lu
; Xiong-lin Luo
; | | Date: |
1 Jan 2022 | | Abstract: | 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. | | Source: | arXiv, 2201.00171 | | Services: | Forum | Review | PDF | Favorites | |
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