CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two c...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 29; číslo 2; s. 440 - 456
Hlavní autoři: Yu, Yinan, Diamantaras, Konstantinos I., McKelvey, Tomas, Kung, Sun-Yuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Adaptive algorithms
Adaptive data sampling
adaptive margin
Approximation algorithms
between-class distance
class-specific subspace
Classification
Computational modeling
Computer applications
Computer networks
Data acquisition
Data processing
Distributed processing
Hilbert space
Kernel
kernel approximation
Kernels
large scale
Learning
Minimization
Optimization
Parallel processing
Representations
Risk reduction
sequential and parallel framework
State of the art
Storage capacity
Subspaces
support vector machine
Test procedures
Training
Training data
ISSN:2162-237X, 2162-2388, 2162-2388
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Popis
Shrnutí:In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2016.2619399