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

Yinan Yu, Konstantinos I. Diamantaras, Tomas McKelvey, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Article number7776910
Pages (from-to)440-456
Number of pages17
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number2
StatePublished - Feb 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


  • Adaptive data sampling
  • adaptive margin
  • between-class distance
  • class-specific subspace
  • classification
  • kernel approximation
  • large scale
  • sequential and parallel framework
  • support vector machine


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