Structure-Aware Data Consolidation

Shihao Wu, Peter Bertholet, Hui Huang, Daniel Cohen-Or, Minglun Gong, Matthias Zwicker

Research output: Contribution to journalArticlepeer-review

Abstract

We present a structure-aware technique to consolidate noisy data, which we use as a pre-process for standard clustering and dimensionality reduction. Our technique is related to mean shift, but instead of seeking density modes, it reveals and consolidates continuous high density structures such as curves and surface sheets in the underlying data while ignoring noise and outliers. We provide a theoretical analysis under a Gaussian noise model, and show that our approach significantly improves the performance of many non-linear dimensionality reduction and clustering algorithms in challenging scenarios.

Original languageEnglish
Article number8046026
Pages (from-to)2529-2537
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume40
Issue number10
DOIs
StatePublished - 1 Oct 2018

Keywords

  • Data consolidation
  • clustering
  • dimensionality reduction
  • filtering
  • manifold denoising

Fingerprint

Dive into the research topics of 'Structure-Aware Data Consolidation'. Together they form a unique fingerprint.

Cite this