Abstract The performance of self-representation based methods is affected by redundant high-dimensional features. Therefore, a subspace clustering method via joint feature selection and smooth representation(FSSR) is proposed in this paper. Firstly, the idea of feature selection is integrated into the self-representation based coefficient matrix learning framework. Meanwhile, a weight factor is adopted to measure different contributions of correlated features. Furthermore, a group effectiveness constraint is imposed on the coefficient matrix for the preservation of locality property. An alternating direction method of multipliers(ADMM) based algorithm is derived to optimize the proposed cost function. Experiments are conducted on synthetic data and standard databases and the results demonstrate that FSSR outperforms the state-of-the-art approaches in both accuracy and efficiency.
Corresponding Authors:
CHEN Wanjun, master, lecturer. Her research interests include machine learning and pattern recognition.
About author:: ZHENG Jianwei, Ph.D., associate professor. His research interests include machine learning and pattern recognition. LU Cheng, master student. His research interests include machine learning and data mining. QIN Mengjie, master student. Her research interests include machine learning and pattern recognition.
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