Description: This paper presents a clustering approach
which estimates the specific subspace and the intrinsic dimension of each class. Our approach
adapts the Gaussian mixture model framework to high-dimensional data and estimates
the parameters which best fit the data. We obtain a robust clustering method called High-
Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images
in a probabilistic framework. Experiments on a recently proposed database demonstrate the
effectiveness of our clustering method for category localization.
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High Dimensional Data Clustering.pdf
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