One-Class Classification Using ℓp-Norm Multiple Kernel Fisher Null Approach

We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an ℓ p -norm regularisation ( p ≥ 1) is considered...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 32; p. 1
Main Author: Arashloo, Shervin Rahimzadeh
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Classification
Kernel
Kernels
Machine learning
multiple kernel learning
One-class classification
one-class Fisher null transformation
Optimization
Pattern recognition
Regularization
Saddle points
Search problems
Support vector machines
Task analysis
Training
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ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an ℓ p -norm regularisation ( p ≥ 1) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2023.3255102