Image analysis with nonlinear adaptive dimension reduction

ICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and Service(2011)

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Abstract
In multimedia applications, dimension reduction is essential to the effectiveness and efficiency of an algorithm due to the curse of dimensionality. Recently, its adaptive variants have received considerable attention in unsupervised learning since a single pass without label information often fails to guarantee an optimal representation, especially when the parameters are not set properly. However, most such methods are basically linear, therefore unable to consider the geometrical structure of the data space. In this paper, we propose a novel algorithm called Nonlinear Adaptive Dimension Reduction (NADR), which adaptively learns the optimal low-dimensional coordinates that preserve the intrinsic geometric structure of the original data. Moreover, the incorporation of K-means enables NADR to be a powerful alternative for cluster analysis. Experiments on benchmark image data sets illustrate that NADR outperforms the state-of-the-art adaptive dimension reduction methods.
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Key words
original data,benchmark image data set,adaptive variant,dimension reduction,nonlinear adaptive dimension reduction,intrinsic geometric structure,data space,novel algorithm,optimal representation,geometrical structure,image analysis,optimal low-dimensional,k means,adaptive learning,curse of dimensionality,unsupervised learning,graph laplacian,cluster analysis
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