Multi-view low-rank sparse subspace clustering based on adaptive dictionary learning

Xiang Li; Congzhe You

doi:10.1117/12.2635363

6 May 2022 Multi-view low-rank sparse subspace clustering based on adaptive dictionary learning

Xiang Li, Congzhe You

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 122560G (2022) https://doi.org/10.1117/12.2635363
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

With the development of science and technology, image processing gradually develops towards higher dimensions. High-dimensional data is usually regarded as approximated by the union of multiple low-dimensional data. That is, the high-dimensional subspace is divided into several low-dimensional subspaces, so as to provide better insights for understanding the underlying structure of the high-dimensional subspace. Most of the existing clustering methods solve the problem of Multi-view subspace clustering by constructing an affinity matrix on each view. This paper proposes a Multi-view low-rank sparse subspace clustering based on adaptive dictionary learning(ADLMLRSSC). In the multi-view low rank sparse representation model, an adaptive dictionary learning strategy using orthogonal constraints is introduced. The dictionary learns adaptively from the original data, which makes the model robust to noise. At the same time, the projection matrix and sparse low rank features are obtained by the optimization method. In theory, low rank and sparse constraints can consider both the global and local structure of the matrix.

Citation Download Citation

Xiang Li and Congzhe You "Multi-view low-rank sparse subspace clustering based on adaptive dictionary learning", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 122560G (6 May 2022); https://doi.org/10.1117/12.2635363

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KEYWORDS

Associative arrays

Detection and tracking algorithms

Databases

Image processing

Pollution

Computer engineering

Convex optimization

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