Unsupervised Locally Embedded Clustering for Automatic High-Dimensional Data Labeling
Yun Fu, Thomas S. Huang · 2007
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.