Paper Info
Title
Selective multiple kernel learning for classification with ensemble strategy.
Abstract
Multiple Kernel Learning (MKL) aims to seek a better result than single kernel learning by combining a compact set of sub-kernels. However, MKL with L1-norm easily discards the sub-kernels with complementary information and MKL with Lp-norm(p≥2) often gets the redundant solution. To address these problems, a Selective Multiple Kernel Learning (SMKL) method, inspired by Ensemble Learning (EL), is proposed. Comparing MKL with Lp-norm(p≥2), SMKL obtains a sparse solution by a pre-selection procedure. Comparing MKL with L1-norm, SMKL preserves the sub-kernels with complementary information by guaranteeing the high discrimination and large diversity of pre-selected sub-kernels. For quantifying the discrimination and diversity of sub-kernels, a new kernel evaluation is designed. SMKL reduces the scale of MKL optimization and saves the memory storing of the sub-kernels, which extends the scale of problem that MKL could solve. Specially, a fast SMKL method using L∞-norm constraint is focused, which needs no MKL optimization process. It means that the memory is hardly a limitation for MKL with the large scale problem. Experiments state that our method is effective for classification.
Year
DOI
Venue
2013
10.1016/j.patcog.2013.04.003
Pattern Recognition
Keywords
Field
DocType
Ensemble learning,Kernel evaluation,Multiple kernel learning,Selective multiple kernel learning,Fast selective multiple kernel learning
Kernel (linear algebra),Radial basis function kernel,Pattern recognition,Multiple kernel learning,Compact space,Tree kernel,Polynomial kernel,Artificial intelligence,Ensemble learning,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
46
11
0031-3203
Citations 
PageRank 
References 
9
0.48
13
Authors
5
Name
Order
Citations
PageRank
Tao Sun116816.47
Licheng Jiao25698475.84
Fang Liu31188125.46
Shuang Wang431639.83
Jie Feng524720.11