文件名称:High
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- 上传时间：2012-11-16
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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.
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