CMBL: Covariance Maximized Basis Learner for nonlinear feature extraction

Abstract

Feature extraction is an essential preprocessing for accurate classification and recognition in machine learning tasks. Recently, deep learning methods have shown high performance on feature extraction. However, they require much time and effort for tuning many hyper-parameters. The Basis Learner, proposed by Livni et al., is a multi-layer feature extractor with much fewer hyper-parameters. In this paper, we propose an improved version of the Basis Learner, named Covariance Maximized Basis Learner, which yields better classification accuracy with even fewer hyper-parameters and lower feature dimensionality.