Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks

18 Aug 2015Aren JansenGregory SellVince Lyzinski

Several popular graph embedding techniques for representation learning and dimensionality reduction rely on performing computationally expensive eigendecompositions to derive a nonlinear transformation of the input data space. The resulting eigenvectors encode the embedding coordinates for the training samples only, and so the embedding of novel data samples requires further costly computation... (read more)

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