Sparse Robust Classification via the Kernel Mean

2015/06/04 by Brendan van Rooyen, Aditya Krishna Menon, van Rooyen, Brendan +2 · 2 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Machine Learning and Data Classification #Machine Learning and Algorithms

paper · pdf · doi:10.48550/arxiv.1506.01520

Abstract

Many leading classification algorithms output a classifier that is a weighted average of kernel evaluations. Optimizing these weights is a nontrivial problem that still attracts much research effort. Furthermore, explaining these methods to the uninitiated is a difficult task. Letting all the weights be equal leads to a conceptually simpler classification rule, one that requires little effort to motivate or explain, the mean. Here we explore the consistency, robustness and sparsification of this simple classification rule.

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