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

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.