NIPS Proceedingsβ

Space-Time Local Embeddings

Part of: Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Authors

Conference Event Type: Poster

Abstract

Space-time is a profound concept in physics. This concept was shown to be useful for dimensionality reduction. We present basic definitions with interesting counter-intuitions. We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space. We apply this concept to manifold learning for preserving local information. Empirical results on non-metric datasets show that more information can be preserved in space-time.