Recursive Similarity-Based Algorithm for Deep Learning

Abstract

Recursive Similarity-Based Learning algorithm (RSBL) follows the deep learning idea, exploiting similarity-based methodology to recursively generate new features. Each transformation layer is generated separately, using as inputs information from all previous layers, and as new features similarity to the k nearest neighbors scaled using Gaussian kernels. In the feature space created in this way results of various types of classifiers, including linear discrimination and distance-based methods, are significantly improved. As an illustrative example a few non-trivial benchmark datasets from the UCI Machine Learning Repository are analyzed.