  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.
