Research on Clustering Performance of Sparse Subspace Clustering
This work addresses the problem of improving clustering reliability for high-dimensional data analysis, but it is incremental as it builds on existing algorithms without introducing new methods.
The paper investigated how different methods for solving the coefficient matrix and constructing the affinity matrix affect clustering accuracy and stability in sparse subspace clustering, finding that both factors significantly influence performance, though no specific numerical results were provided.
Recently, sparse subspace clustering has been a valid tool to deal with high-dimensional data. There are two essential steps in the framework of sparse subspace clustering. One is solving the coefficient matrix of data, and the other is constructing the affinity matrix from the coefficient matrix, which is applied to the spectral clustering. This paper investigates the factors which affect clustering performance from both clustering accuracy and stability of the approaches based on existing algorithms. We select four methods to solve the coefficient matrix and use four different ways to construct a similarity matrix for each coefficient matrix. Then we compare the clustering performance of different combinations on three datasets. The experimental results indicate that both the coefficient matrix and affinity matrix have a huge influence on clustering performance and how to develop a stable and valid algorithm still needs to be studied.