Supervised Nonlinear Dimensionality Reduction by Neighbor Retrieval

Reference:

Jaakko Peltonen, Helena Aidos, and Samuel Kaski. Supervised nonlinear dimensionality reduction by neighbor retrieval. In Proceedings of ICASSP 2009, the IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 1809–1812. IEEE, 2009.

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.

Suggested BibTeX entry:

@inproceedings{Peltonen09icassp,
    author = {Jaakko Peltonen and Helena Aidos and Samuel Kaski},
    booktitle = {Proceedings of ICASSP 2009, the IEEE International Conference on Acoustics, Speech, and Signal Processing},
    pages = {1809-1812},
    publisher = {IEEE},
    title = {Supervised Nonlinear Dimensionality Reduction by Neighbor Retrieval},
    year = {2009},
}