Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality

February 5, 2020 Abstract In the past decade, interest in generative models has grown tremendously. However, their training performance can be highly affected by contamination, where outliers are encoded in the representation of the model. In this paper, we introduce a weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs). 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. Keywords-- Machine Learning, Generative Models, Robustness, Kernel Methods, Restricted Kernel Machines 1 Introduction Generative modeling is an important direction within machine learning, finding applications in image generation [1], anomaly detection [2], denoising [3], collaborative filtering [4] and many more. A popular choice for generation are latent variable models like V ariational Auto-Encoders (V AEs) [5] and Restricted Boltzmann Machines (RBMs) [6, 7].