Memory-Efficient Sampling for Minimax Distance Measures

TL;DR

This paper introduces a novel sampling technique that reduces the memory complexity of Minimax distance measures from quadratic to linear, enabling more scalable unsupervised pattern analysis.

Contribution

It proposes an adaptive sampling method specifically designed for Minimax distances, significantly reducing memory requirements while maintaining performance.

Findings

The new sampling method achieves linear space complexity.

Experimental results demonstrate effective pattern extraction on real-world datasets.

The approach outperforms traditional quadratic-memory methods.

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.