Semi-supervised high-dimensional clustering by tight wavelet frames

Bin Dong, Ning Hao

Research output: Chapter in Book/Report/Conference proceedingConference contribution


High-dimensional clustering arises frequently from many areas in natural sciences, technical disciplines and social medias. In this paper, we consider the problem of binary clustering of high-dimensional data, i.e. classification of a data set into 2 classes. We assume that the correct (or mostly correct) classification of a small portion of the given data is known. Based on such partial classification, we design optimization models that complete the clustering of the entire data set using the recently introduced tight wavelet frames on graphs.1 Numerical experiments of the proposed models applied to some real data sets are conducted. In particular, the performance of the models on some very high-dimensional data sets are examined; and combinations of the models with some existing dimension reduction techniques are also considered.

Original languageEnglish (US)
Title of host publicationWavelets and Sparsity XVI
EditorsVivek K. Goyal, Dimitri Van De Ville, Dimitri Van De Ville, Manos Papadakis, Dimitri Van De Ville, Manos Papadakis, Vivek K. Goyal, Dimitri Van De Ville
ISBN (Electronic)9781628417630, 9781628417630
StatePublished - 2015
EventWavelets and Sparsity XVI - San Diego, United States
Duration: Aug 10 2015Aug 12 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherWavelets and Sparsity XVI
Country/TerritoryUnited States
CitySan Diego


  • Graph clustering
  • High-dimensional data analysis
  • Sparse representation on graphs
  • Spectral graph theory
  • Tight wavelet frames

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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