A weighted information criterion for multiple minor components and its adaptive extraction algorithms

• Published in: Neural networks Vol. 89; pp. 1 - 10
• Main Authors: Gao, Yingbin, Kong, Xiangyu, Zhang, Huihui, Hou, Li’an
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2017
• Subjects: Algorithms; Computer Simulation; Global convergence; Lyapunov method; Multiple minor components extraction; Neural networks; Neural Networks (Computer); Pattern Recognition, Automated - methods; Recursive least square; Signal Processing, Computer-Assisted; Weighted information criterion; Lyapunov method; Weighted information criterion; Recursive least square; Global convergence; Neural networks; Multiple minor components extraction
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2017.02.006

Minor component (MC) plays an important role in signal processing and data analysis, so it is a valuable work to develop MC extraction algorithms. Based on the concepts of weighted subspace and optimum theory, a weighted information criterion is proposed for searching the optimum solution of a linear neural network. This information criterion exhibits a unique global minimum attained if and only if the state matrix is composed of the desired MCs of an autocorrelation matrix of an input signal. By using gradient ascent method and recursive least square (RLS) method, two algorithms are developed for multiple MCs extraction. The global convergences of the proposed algorithms are also analyzed by the Lyapunov method. The proposed algorithms can extract the multiple MCs in parallel and has advantage in dealing with high dimension matrices. Since the weighted matrix does not require an accurate value, it facilitates the system design of the proposed algorithms for practical applications. The speed and computation advantages of the proposed algorithms are verified through simulations.