Open AccessIMAT: matrix learning machine with interpolation mapping
Author(s) -
Wang Zhe,
Lu Mingzhe,
Zhu Yujin,
Gao Daqi
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2014.2747
Subject(s) - pattern recognition (psychology) , artificial intelligence , computer science , support vector machine , pairwise comparison , classifier (uml) , matrix (chemical analysis) , interpolation (computer graphics) , algorithm , image (mathematics) , materials science , composite material
In matrix learning, vector patterns are simply transformed into matrix ones by some reshaping techniques such as from 100 × 1 to 20 × 5. Unfortunately, the techniques are random and fail in some cases. To this end, a matrix learning machine with interpolation mapping named IMAT for short is proposed. IMAT interpolates each feature of the original vector pattern into its corresponding k ‐means slots so as to generate a matrix pattern with more structural information. Furthermore, the pairwise information of every two features can be introduced into the IMAT. After that, the IMAT can be applied into matrix‐based classifiers. The contributions of the proposed IMAT are listed as follows. (i) the IMAT can extract more intrinsic structural information compared with those random techniques reshaping the vector into a matrix. (ii) The IMAT is supposed to be reasonably and naturally embedded into matrix‐based classifiers. In the experiments, the authors' previous work is adopted, a matrix‐based classifier named MatMHKS, to examine the IMAT on some UCI datasets. The results verify the superior classification performance of IMAT.