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http://dbpedia.org/resource/Semidefinite_embedding
http://dbpedia.org/ontology/abstract Maximum Variance Unfolding (MVU), also knoMaximum Variance Unfolding (MVU), also known as Semidefinite Embedding (SDE), is an algorithm in computer science that uses semidefinite programming to perform non-linear dimensionality reduction of high-dimensional vectorial input data. It is motivated by the observation that kernel Principal Component Analysis (kPCA) does not reduce the data dimensionality, as it leverages the Kernel trick to non-linearly map the original data into an inner-product space.original data into an inner-product space.
http://dbpedia.org/ontology/wikiPageExternalLink https://repository.upenn.edu/cis_papers/1/ + , https://repository.upenn.edu/cis_papers/2/ + , http://bicmr.pku.edu.cn/~wenzw/bigdata/weinberger-saul-mvu-image-manifolds.pdf + , http://www.jmlr.org/papers/v13/lawrence12a.html%7Cbibcode=2010arXiv1010.4830L%7Carxiv=1010.4830 + , https://web.archive.org/web/20120313162120/http:/www.cse.wustl.edu/~kilian/code/code.html + , https://www.researchgate.net/publication/228057833 +
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rdfs:comment Maximum Variance Unfolding (MVU), also knoMaximum Variance Unfolding (MVU), also known as Semidefinite Embedding (SDE), is an algorithm in computer science that uses semidefinite programming to perform non-linear dimensionality reduction of high-dimensional vectorial input data. It is motivated by the observation that kernel Principal Component Analysis (kPCA) does not reduce the data dimensionality, as it leverages the Kernel trick to non-linearly map the original data into an inner-product space.original data into an inner-product space.
rdfs:label Semidefinite embedding
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