# Unsupervised MKL in Multi-layer Kernel Machines

arXiv: https://arxiv.org/abs/2111.13769 · HTML: https://mlsanity.com/paper/2111.13769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Fractional Max-Pooling](https://mlsanity.com/paper/1412.6071/) (2014)
- [GraphLab: A New Framework For Parallel Machine Learning](https://mlsanity.com/paper/1408.2041/) (2014)

## Abstract (arXiv)

Kernel based Deep Learning using multi-layer kernel machines(MKMs) was proposed by Y.Cho and L.K. Saul in citesaul. 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 textitmnist-back-rand, textitmnist-back-image and textitmnist-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.