文件名称:sdp_jmlr_submit
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We propose a novel fr a mework for thedeterministicconstruction oflinear, near-isometric
embeddings of a nite set of data points. -We demonstrate that our fr a mework
is useful for a number of applications in machine learning and signal processing via a range
of experiments on large-scale synthetic and real datasets.
embeddings of a nite set of data points. -We demonstrate that our fr a mework
is useful for a number of applications in machine learning and signal processing via a range
of experiments on large-scale synthetic and real datasets.
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sdp_jmlr_submit.pdf