Anisotropic Agglomerative Adaptive Mean-Shift

TL;DR

This paper introduces an adaptive anisotropic Mean Shift clustering method that automatically adjusts local bandwidths for improved mode detection and clustering, especially in low-dimensional spaces.

Contribution

It presents a novel unsupervised, online approach for anisotropic clustering with adaptive bandwidth matrices that evolve through agglomeration, enhancing flexibility and detail preservation.

Findings

Effective in low-dimensional feature spaces

Reduces dependence on manual bandwidth selection

Improves clustering detail and salience

Abstract

Mean Shift today, is widely used for mode detection and clustering. The technique though, is challenged in practice due to assumptions of isotropicity and homoscedasticity. We present an adaptive Mean Shift methodology that allows for full anisotropic clustering, through unsupervised local bandwidth selection. The bandwidth matrices evolve naturally, adapting locally through agglomeration, and in turn guiding further agglomeration. The online methodology is practical and effecive for low-dimensional feature spaces, preserving better detail and clustering salience. Additionally, conventional Mean Shift either critically depends on a per instance choice of bandwidth, or relies on offline methods which are inflexible and/or again data instance specific. The presented approach, due to its adaptive design, also alleviates this issue - with a default form performing generally well. The methodology though, allows for effective tuning of results.