Multi-Scale Local Shape Analysis and Feature Selection in Machine Learning Applications

TL;DR

This paper presents MLSA, a multi-scale local shape analysis technique that extracts geometric and topological features to improve classification performance in machine learning tasks.

Contribution

The paper introduces MLSA, a novel multi-scale feature extraction method that enhances local structure representation for machine learning applications.

Findings

Significant performance improvements in classification accuracy.

Effective capture of diverse local information.

Validated on synthetic and real datasets.

Abstract

We introduce a method called multi-scale local shape analysis, or MLSA, for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine learning algorithms operating on the dataset. Using synthetic and real dataset examples, we demonstrate significant performance improvement of classification algorithms constructed for these datasets with correspondingly augmented features.