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- W2113627413 abstract "We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an “out-of-sample” problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.KeywordsSpectral ClusterNeural Information Processing SystemClass ImbalanceLinear NetworkStochastic Gradient DescentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W2113627413 date "2008-08-13" @default.
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- W2113627413 title "Large-Scale Clustering through Functional Embedding" @default.
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