Open AccessL1‐norm based discriminant manifold learning for multi‐label image classification
Author(s) -
Cheng Jiafeng,
Deng Siyang,
Xia Wei,
Liu Yang
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1150
Subject(s) - dimensionality reduction , pattern recognition (psychology) , discriminant , linear discriminant analysis , nonlinear dimensionality reduction , artificial intelligence , norm (philosophy) , computer science , curse of dimensionality , mathematics , mathematical optimization , political science , law
Recently, L1‐norm based robust discriminant feature extraction technique has been attracted much attention in dimensionality reduction. However, most existing approaches solve the column vectors of the optimal projection matrix one by one with a greedy strategy. Moreover, they are not suitable for solving the multi‐label image classification. To solve these problems, the authors give a model named L1‐norm based discriminant manifold learning in this study. An iterative non‐greedy algorithm is proposed to solve the objective and the obtained optimal projection matrix necessarily best optimise the corresponding trace ratio objective function, which is the essential criterion function for general supervised dimensionality reduction. They also analyse the convergence of the authors’ proposed algorithm in detail. Extensive experiments on some databases illustrate the effectiveness of their proposed method.