An unsupervised feature selection method is proposed for analysis of data sets of high dimensionality. The Least Square Error (LSE) of approximating the complete data set via a reduced feature subset is proposed as the quality measure for feature selection. Guided by this LSE criterion, a feature selection algorithm is developed to find the feature subset with the lowest LSE. The algorithm (named KLS-FS) is granted the capability of non-linear feature selection by using the kernel representation. An incremental LSE computation is designed to accelerate the selection process and, therefore, enhances the scalability of KLS-FS to high-dimensional datasets. The superiority of the proposed feature selection algorithm, in terms of keeping principal data structures, learning performances in classification and clustering applications, and robustness, is demonstrated using various real-life datasets of different sizes and dimensions.
Liu R, Rallo R, Cohen Y. (2011) Unsupervised Feature Selection using Incremental Least Squares. International Journal of Information and Decision Making, 10(6):967-987
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