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In projective clustering we are given a set of n points in $R^d$ and wish to cluster them to a set $S$ of $k$ linear subspaces in $R^d$ according to some given distance function.
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N. Halko, P.-G. Martinsson, and J. A. Tropp · 2011
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https://gist.github.com/h3xx/1976236 , 2012
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Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering
D. Feldman, M. Schmidt, and C. Sohler · 2013
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Dimensionality reduction for k-means clustering and low rank approximation
M. B. Cohen, S. Elder, C. Musco, C. Musco, and M. Persu · 2015
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Optimal approximate matrix product in terms of stable rank
M. B. Cohen, J. Nelson, and D. P. Woodruff · 2015
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New frameworks for offline and streaming coreset constructions
V. Braverman, D. Feldman, and H. Lang · 2016
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Dimensionality reduction of massive sparse datasets using coresets
D. Feldman, M. Volkov, and D. Rus · 2016
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A public domain dataset for human activity recognition using smartphones
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz · 2013
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https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
UCI
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A. Bhattacharya and R. Jaiswal · 2017
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Low-rank approximation and regression in input sparsity time
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https://dumps.wikimedia.org/enwiki/latest/ , 2019
2019
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k-means+++: Outliers-resistant clustering
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