Kernel optimization using nonparametric Fisher criterion in the subspace

•We solve the kernel optimization problem by creating an object function in the subspace instead of kernel space.•We propose a new separability measure as the object function, named nonparametric Fisher criterion.•We show that the KNDA algorithm with all parameters optimized can get the best perform...

Full description

Saved in:
Bibliographic Details
Published in:Pattern recognition letters 2015-03, Vol.54, p.43-49
Main Authors: Wu, Xingyu, Mao, Xia, Chen, Lijiang, Xue, Yuli, Rovetta, Alberto
Format: Article
Language:English
Subjects:
Dimensionality reduction
Fisher criterion
Kernel optimization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•We solve the kernel optimization problem by creating an object function in the subspace instead of kernel space.•We propose a new separability measure as the object function, named nonparametric Fisher criterion.•We show that the KNDA algorithm with all parameters optimized can get the best performance. Kernel optimization plays an important role in kernel-based dimensionality reduction algorithms, such as kernel principal components analysis (KPCA) and kernel discriminant analysis (KDA). In this paper, a nonparametric Fisher criterion is proposed as the objective function to find the optimized kernel parameters. Unlike other criterions that rooted in the kernel feature space, the proposed criterion works in the low-dimensional subspace to measure the separability of different patterns. Experiments on 13 different benchmark datasets show the effectiveness of the proposed method, in comparison with other criterions and the kernel space methods.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2014.11.016