Fast Non-Linear Dimension Reduction

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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Nanda Kambhatla, Todd Leen


We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Exper(cid:173) iments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also more than an order of magnitude faster to train.