Memory-Efficient Sampling for Minimax Distance Measures
Machine Learning
2020-05-27 v1 Machine Learning
Abstract
Minimax distance measure extracts the underlying patterns and manifolds in an unsupervised manner. The existing methods require a quadratic memory with respect to the number of objects. In this paper, we investigate efficient sampling schemes in order to reduce the memory requirement and provide a linear space complexity. In particular, we propose a novel sampling technique that adapts well with Minimax distances. We evaluate the methods on real-world datasets from different domains and analyze the results.
Cite
@article{arxiv.2005.12627,
title = {Memory-Efficient Sampling for Minimax Distance Measures},
author = {Fazeleh Sadat Hoseini and Morteza Haghir Chehreghani},
journal= {arXiv preprint arXiv:2005.12627},
year = {2020}
}