CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification
Artikel i vetenskaplig tidskrift, 2018

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.

large scale

class-specific subspace

adaptive margin

sequential and parallel framework

Adaptive data sampling

kernel approximation

support vector machine

between-class distance

classification

Författare

Yinan Yu

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Konstantinos I. Diamantaras

Alexander Technological Educational Institute of Thessaloniki (ATEITH)

Tomas McKelvey

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Sun-Yuan Kung

Princeton University

IEEE Transactions on Neural Networks and Learning Systems

2162-237X (ISSN)

Vol. 29 440 -456

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

DOI

10.1109/TNNLS.2016.2619399