Unsupervised MKL in Multi-layer Kernel Machines
Computer Vision and Pattern Recognition
2021-11-30 v1 Machine Learning
Abstract
Kernel based Deep Learning using multi-layer kernel machines(MKMs) was proposed by Y.Cho and L.K. Saul in \cite{saul}. In MKMs they used only one kernel(arc-cosine kernel) at a layer for the kernel PCA-based feature extraction. We propose to use multiple kernels in each layer by taking a convex combination of many kernels following an unsupervised learning strategy. Empirical study is conducted on \textit{mnist-back-rand}, \textit{mnist-back-image} and \textit{mnist-rot-back-image} datasets generated by adding random noise in the image background of MNIST dataset. Experimental results indicate that using MKL in MKMs earns a better representation of the raw data and improves the classifier performance.
Keywords
Cite
@article{arxiv.2111.13769,
title = {Unsupervised MKL in Multi-layer Kernel Machines},
author = {Akhil Meethal and Asharaf S and Sumitra S},
journal= {arXiv preprint arXiv:2111.13769},
year = {2021}
}