In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction.
http://en.wikipedia.org/wiki/Dimensionality_reduction
Abdullah Albarrak
What about Computer Vision? I have seen couple of papers which address the problem of representing the features of multimedia objects, and how to reduce the dimensionality of those features to save space & processing time. The same case applies to spatial objects in spatial databases.
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Nikolay Sergievskiy
Non-linear methods based on neural networks like autoassociative, autoencoders, RBM.
Nice example is tSne http://homepage.tudelft.nl/19j49/t-SNE.html (but it is offtopic)
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