Fast trace ratio-based feature selection Online publication date: Tue, 09-Aug-2022
by Bo Liu; Ling Ling Tao; Xi Ping He
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 22, No. 3/4, 2022
Abstract: The quality of features has a great impact on machine learning tasks. Feature selection obtains a high-quality feature subset from data, which has been widely studied because of high interpretability. In this paper, we propose a novel feature selection algorithm called trace Ratio-Based Feature Selection (RBFS), which first defines the distance of different classes and the same classes for a given sample and then projects these distances into the subspace. The margin is defined by the trace ratio of these two distances. The objective function is formulated by maximising the margin. To avoid a trivial solution, the orthogonal subspace and the L2,1 norm are incorporated into the objective function. Then, theoretically, the rewritten objective function can obtain the optimal solution through alternating iterations. In addition, power iteration is introduced to reduce the computational cost. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with six other state-of-the-art ones.
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