# Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning

## TL;DR

This paper presents Gen-RKM, a flexible framework combining kernel methods and neural networks for multi-view data generation and disentangled feature learning, with promising experimental results.

## Contribution

It introduces a unified RKM-based framework for joint multi-view generation and uncorrelated feature learning, integrating kernel and neural network models.

## Key findings

- Successful multi-view data generation demonstrated.
- Uncorrelated feature learning via eigen-decomposition.
- Effective with both kernel and neural network implementations.

## Abstract

This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM. To enable joint multi-view generation, this mechanism uses a shared representation of data from various views. Furthermore, the model has a primal and dual formulation to incorporate both kernel-based and (deep convolutional) neural network based models within the same setting. When using neural networks as explicit feature-maps, a novel training procedure is proposed, which jointly learns the features and shared subspace representation. The latent variables are given by the eigen-decomposition of the kernel matrix, where the mutual orthogonality of eigenvectors represents the learned uncorrelated features. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of generated samples on various standard datasets.