Quiver Laplacians and Feature Selection

arXiv:2404.06993v1 Announce Type: cross
Abstract: The challenge of selecting the most relevant features of a given dataset arises ubiquitously in data analysis and dimensionality reduction. However, features found to be of high importance for the entire dataset may not be relevant to subsets of interest, and vice versa. Given a feature selector and a fixed decomposition of the data into subsets, we describe a method for identifying selected features which are compatible with the decomposition into subsets. We achieve this …

abstract analysis arxiv challenge cs.lg data data analysis dataset dimensionality feature features feature selection found however importance math.co math.rt math.st q-bio.qm stat.ml stat.th subsets type