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

arXiv: https://arxiv.org/abs/1906.08144 · HTML: https://mlsanity.com/paper/1906.08144/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [TRKM: Twin Restricted Kernel Machines for Classification and Regression](https://mlsanity.com/paper/2502.15759/) (2025)
- [Deep Kernel Principal Component Analysis for Multi-level Feature Learning](https://mlsanity.com/paper/2302.11220/) (2023)
- [Latent Space Exploration Using Generative Kernel PCA](https://mlsanity.com/paper/2105.13949/) (2021)
- [Tensor-based Multi-view Spectral Clustering via Shared Latent Space](https://mlsanity.com/paper/2207.11559/) (2022)
- [Multi-view Kernel PCA for Time series Forecasting](https://mlsanity.com/paper/2301.09811/) (2023)
- [A Dual Formulation for Probabilistic Principal Component Analysis](https://mlsanity.com/paper/2307.10078/) (2023)
- [CI-RKM: A Class-Informed Approach to Robust Restricted Kernel Machines](https://mlsanity.com/paper/2504.11476/) (2025)

## Abstract (arXiv)

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.