Subspace clustering with automatic feature grouping. Issue 11 (November 2015) - Gan, Guojun
Abstract: This paper proposes a subspace clustering algorithm with automatic feature grouping for clustering high-dimensional data. In this algorithm, a new component is introduced into the objective function to capture the feature groups and a new iterative process is defined to optimize the objective function so that the features of high-dimensional data are grouped automatically. Experiments on both synthetic data and real data show that the new algorithm outperforms the FG- k -means algorithm in terms of accuracy and choice of parameters. Abstract : Highlights: We study the problem of subspace clustering with feature grouping. We propose a k -means-type algorithm by incorporating feature grouping into the objective function. The algorithm is able to determine feature groups automatically. Experiments on synthetic and real data show that the algorithm performs well.