Memory-Efficient Sampling for Minimax Distance Measures
This work addresses memory efficiency for researchers and practitioners using minimax distance measures in unsupervised learning, though it appears incremental as it builds on existing methods.
The paper tackles the quadratic memory requirement of existing minimax distance methods by proposing a novel sampling technique that achieves linear space complexity, and it evaluates the approach on real-world datasets from various domains.
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.