Large-Scale Mode Identification and Data-Driven Sciences
Abstract
Bump-hunting or mode identification is a fundamental problem that arises in almost every scientific field of data-driven discovery. Surprisingly, very few data modeling tools are available for automatic (not requiring manual case-by-base investigation), objective (not subjective), and nonparametric (not based on restrictive parametric model assumptions) mode discovery, which can scale to large data sets. This article introduces LPMode--an algorithm based on a new theory for detecting multimodality of a probability density. We apply LPMode to answer important research questions arising in various fields from environmental science, ecology, econometrics, analytical chemistry to astronomy and cancer genomics.
Cite
@article{arxiv.1509.06428,
title = {Large-Scale Mode Identification and Data-Driven Sciences},
author = {Subhadeep Mukhopadhyay},
journal= {arXiv preprint arXiv:1509.06428},
year = {2016}
}
Comments
I would like to express my sincere thanks to the Editor and the anonymous reviewers for their in-depth comments, which have greatly improved the manuscript