Semi-supervised high-dimensional clustering by tight wavelet frames

Bin Dong; Ning Hao

doi:10.1117/12.2187521

Semi-supervised high-dimensional clustering by tight wavelet frames

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English (US)
Title of host publication	Wavelets and Sparsity XVI
Editors	Vivek 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
Publisher	SPIE
ISBN (Electronic)	9781628417630, 9781628417630
DOIs	https://doi.org/10.1117/12.2187521
State	Published - 2015
Event	Wavelets and Sparsity XVI - San Diego, United States Duration: Aug 10 2015 → Aug 12 2015

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	9597
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Other

Other	Wavelets and Sparsity XVI
Country/Territory	United States
City	San Diego
Period	8/10/15 → 8/12/15

Keywords

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

Access to Document

10.1117/12.2187521

Cite this

Dong, B., & Hao, N. (2015). Semi-supervised high-dimensional clustering by tight wavelet frames. In V. K. Goyal, D. Van De Ville, D. Van De Ville, M. Papadakis, D. Van De Ville, M. Papadakis, V. K. Goyal, & D. Van De Ville (Eds.), Wavelets and Sparsity XVI Article 959708 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9597). SPIE. https://doi.org/10.1117/12.2187521

Semi-supervised high-dimensional clustering by tight wavelet frames. / Dong, Bin; Hao, Ning.
Wavelets and Sparsity XVI. ed. / Vivek 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. SPIE, 2015. 959708 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9597).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Dong, B & Hao, N 2015, Semi-supervised high-dimensional clustering by tight wavelet frames. in VK Goyal, D Van De Ville, D Van De Ville, M Papadakis, D Van De Ville, M Papadakis, VK Goyal & D Van De Ville (eds), Wavelets and Sparsity XVI., 959708, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9597, SPIE, Wavelets and Sparsity XVI, San Diego, United States, 8/10/15. https://doi.org/10.1117/12.2187521

Dong, Bin ; Hao, Ning. / Semi-supervised high-dimensional clustering by tight wavelet frames. Wavelets and Sparsity XVI. editor / Vivek 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. SPIE, 2015. (Proceedings of SPIE - The International Society for Optical Engineering).

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Semi-supervised high-dimensional clustering by tight wavelet frames

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Keywords

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