# Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality

arXiv: https://arxiv.org/abs/2002.01180 · HTML: https://mlsanity.com/paper/2002.01180/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine](https://mlsanity.com/paper/2102.08443/) (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)
- [Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers](https://mlsanity.com/paper/2306.07015/) (2023)
- [A Dual Formulation for Probabilistic Principal Component Analysis](https://mlsanity.com/paper/2307.10078/) (2023)

## Abstract (arXiv)

Interest in generative models has grown tremendously in the past decade. However, their training performance can be adversely affected by contamination, where outliers are encoded in the representation of the model. This results in the generation of noisy data. In this paper, we introduce weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs). The RKM formulation allows for an easy integration of methods from classical robust statistics. This formulation is used to fine-tune the latent space of generative RKMs using a weighting function based on the Minimum Covariance Determinant, which is a highly robust estimator of multivariate location and scatter. Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data. We further show that the robust method also preserves uncorrelated feature learning through qualitative and quantitative experiments on standard datasets.