Unsupervised locally embedded clustering for automatic high-dimensional data labeling

Yun Fu, Thomas S Huang

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

Abstract

In most machine learning and pattern recognition problems, the large number of high-dimensional sensory data, such as images and videos, are often labeled manually for training classifiers and modeling features, which is time-consuming and tedious. To automatically execute this process by machine, we present the unsupervised high-dimensional data clustering and automatic labeling algorithms, called Locally Embedded Clustering (LEC): (i) Constructing the neighborhood weighted graph with an appropriate distance metric; (ii) Tuning the regularization parameter to smooth the approximated manifold; (iii) Calculating the unified projection in a closed-form solution for the embedding and dimensionality reduction; (iv) Choosing the top or bottom coordinates of the embedded low-dimensional space for data representation; (v) Normalizing the low-dimensional representation to have unit length; (vi) Clustering and labeling the data via K-means. Experimental results demonstrate that LEC provides better data representation, more efficient dimensionality reduction and better clustering performance than many existing methods.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Volume3
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/TerritoryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Dimensionality reduction
  • High-dimensional data clustering
  • LEA
  • LEC
  • Manifold

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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