By Topic

Unsupervised Locally Embedded Clustering for Automatic High-Dimensional Data Labeling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yun Fu ; Beckman Inst. for Adv. Sci. & Technol., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA ; Huang, T.S.

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.

Published in:

Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on  (Volume:3 )

Date of Conference:

15-20 April 2007