Journal:

International Journal Of Wireless And Mobile Computing

Key Words:

feature selection, trace ratio criteria, large margin, power iteration

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.

Indexed by:

Journal paper

Discipline:

Engineering

First-Level Discipline:

Computer Science and Technology

Document Type:

Journal Article

Volume:

22

Issue:

3-4

Page Number:

265-273

Translation or Not:

no

Date of Publication:

2022-08-09

Included Journals:

EI