Multi-Scale Local Shape Analysis and Feature Selection in Machine Learning Applications
This work addresses feature extraction for machine learning applications, but it appears incremental as it builds on existing multi-scale and shape analysis techniques.
The paper tackles the problem of extracting local structural features from datasets by introducing multi-scale local shape analysis (MLSA), which uses geometric and topological features at multiple granularities, and demonstrates significant performance improvements in classification algorithms on synthetic and real datasets.
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.