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Memory-Efficient Sampling for Minimax Distance Measures

2020-05-26 · Fazeleh Sadat Hoseini, Morteza Haghir Chehreghani

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.

📄 PDF Abstract BibTeX arXiv:2005.12627

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