  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.
