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Supervised Dimensionality Reduction by Neighbor Retrieval

Peltonen, J. ; Aidos, H. ; Kaski, S.

Supervised Dimensionality Reduction by Neighbor Retrieval, Proc IEEE International Conf. on Acoustics, Speech, and Signal Processing - ICASSP, Taipei, Taiwan, Vol. 0, pp. 1809 - 1812, April, 2009.

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Abstract
Many recent works have combined two machine learning topics, learning of supervised distance metrics and manifold embedding methods, into supervised nonlinear dimensionality reduction methods. We show that a combination of an early metric learning method and a recent unsupervised dimensionality reduction method empirically outperforms previous methods. In our method, the Riemannian distance metric measures local change of class distributions, and the dimensionality reduction method makes a rigorous tradeoff between precision and recall in retrieving similar data points based on the reduced-dimensional display. The resulting supervised visualizations are good for finding (sets of) similar data samples that have similar class distributions.