Abstract
Subspace clustering (SC) achieves excellent clustering result via learning an affinity matrix and applying the spectral clustering on the matrix in order to divide data points into several different subspaces. One of the challenges faced by various SC methods is to construct a fitting affinity matrix from the given data. Sparse subspace clustering (SSC) learns the affinity matrix by minimizing l1 norm. Log-determinant approximation to rank (CLAR) learns the affinity matrix by employing a special function, called Log-determinant (LogDet), to take the place of rank function. In this paper, we propose an improved SSC algorithm by using LogDet function and Frobenius norm. The improved algorithm could effectively improve the grouping effect and obtain block diagonal solution, especially when the database size is large. Experiments on the Synthetic database, the ‘Hopkins155’ database and ‘Extended Yale B’ database, are demonstrations of the proposed approach could get a better clustering result.
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Yu, Q., Zhang, Y., Sun, C. (2020). Efficient Subspace Clustering Based on Enhancing Local Structure and Global Structure. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2019. Advances in Intelligent Systems and Computing, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-030-34387-3_45
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DOI: https://doi.org/10.1007/978-3-030-34387-3_45
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