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Efficient Machine Learning using Random Projections

Efficient Machine Learning using Random Projections

This video was recorded at NIPS Workshop on Efficient Machine Learning, Whistler 2007. As an alternative to cumbersome nonlinear schemes for dimensionality reduction, the technique of random linear projection has recently emerged as a viable alternative for storage and rudimentary processing of high-dimensional data. We invoke new theory to motivate the following claim: the random projection method may be used in conjunction with standard algorithms for a multitude of machine learning tasks, with virtually no degradation in performance. Thus, random projections can been shown to result in both significant computational savings and provably good performance.


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