Adaptive Matching of Kernel Means

TL;DR

This paper introduces an adaptive kernel mean matching method that improves data analysis by selecting high-importance data for efficient and scalable learning, outperforming existing methods on real-world datasets.

Contribution

A novel adaptive kernel mean matching approach that enhances efficiency and scalability while maintaining high performance in data analysis tasks.

Findings

Outperforms several state-of-the-art methods

Maintains calculation efficiency

Effective on diverse real-world datasets

Abstract

As a promising step, the performance of data analysis and feature learning are able to be improved if certain pattern matching mechanism is available. One of the feasible solutions can refer to the importance estimation of instances, and consequently, kernel mean matching (KMM) has become an important method for knowledge discovery and novelty detection in kernel machines. Furthermore, the existing KMM methods have focused on concrete learning frameworks. In this work, a novel approach to adaptive matching of kernel means is proposed, and selected data with high importance are adopted to achieve calculation efficiency with optimization. In addition, scalable learning can be conducted in proposed method as a generalized solution to matching of appended data. The experimental results on a wide variety of real-world data sets demonstrate the proposed method is able to give outstanding performance compared with several state-of-the-art methods, while calculation efficiency can be preserved.