Kernel Transform Learning

TL;DR

This paper introduces a kernelized transform learning method that analyzes data through a learned transform, outperforming existing techniques like autoencoders and dictionary learning, especially for large sample sizes.

Contribution

It proposes a novel kernel transform learning approach that efficiently handles large datasets, extending transform analysis into the kernel domain with superior performance.

Findings

Outperforms autoencoder, RBM, and kernel dictionary learning

Effective for large sample sizes with high-dimensional kernels

Validated on benchmark datasets

Abstract

This work proposes kernel transform learning. The idea of dictionary learning is well known; it is a synthesis formulation where a basis is learnt along with the coefficients so as to generate or synthesize the data. Transform learning is its analysis equivalent; the transforms operates or analyses on the data to generate the coefficients. The concept of kernel dictionary learning has been introduced in the recent past, where the dictionary is represented as a linear combination of non-linear version of the data. Its success has been showcased in feature extraction. In this work we propose to kernelize transform learning on line similar to kernel dictionary learning. An efficient solution for kernel transform learning has been proposed especially for problems where the number of samples is much larger than the dimensionality of the input samples making the kernel matrix very high dimensional. Kernel transform learning has been compared with other representation learning tools like autoencoder, restricted Boltzmann machine as well as with dictionary learning (and its kernelized version). Our proposed kernel transform learning yields better results than all the aforesaid techniques; experiments have been carried out on benchmark databases.