ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1404.1425
420
30
v1v2v3v4 (latest)

Density Estimation via Discrepancy Based Adaptive Sequential Partition

5 April 2014
Dangna Li
Kun Yang
W. Wong
ArXiv (abs)PDFHTML
Abstract

Given iidiidiid observations from an unknown absolute continuous distribution defined on some domain Ω\OmegaΩ, we propose a nonparametric method to learn a piecewise constant function to approximate the underlying probability density function. Our density estimate is a piecewise constant function defined on a binary partition of Ω\OmegaΩ. The key ingredient of the algorithm is to use discrepancy, a concept originates from Quasi Monte Carlo analysis, to control the partition process. The resulting algorithm is simple, efficient, and has a provable convergence rate. We empirically demonstrate its efficiency as a density estimation method. We present its applications on a wide range of tasks, including finding good initializations for k-means.

View on arXiv
Comments on this paper