Memory-Efficient Sampling for Minimax Distance Measures

• URL: http://arxiv.org/abs/2005.12627v1
• Date: Tue, 26 May 2020 11:00:34 GMT
• Title: Memory-Efficient Sampling for Minimax Distance Measures
• Authors: Fazeleh Sadat Hoseini, Morteza Haghir Chehreghani
• Abstract summary: In this paper, we investigate efficient sampling schemes in order to reduce the memory requirement and provide a linear space complexity. We evaluate the methods on real-world datasets from different domains and analyze the results.
• Score: 4.873362301533825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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