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$(j,k)$-projective clustering is the natural generalization of the family of $k$-clustering and $j$-subspace clustering problems.
Über den variabilitätsbereich der koeffizienten von potenzreihen, die gegebene werte nicht annehmen
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Steinitz, E. (1913) · 1913
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Fast algorithms for projected clustering
Aggarwal, C. C., Procopiuc, C. M., Wolf, J. L., Yu, P. S., and Park, J. S. (1999) · 1999
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Finding generalized projected clusters in high dimensional spaces
Aggarwal, C. C. and Yu, P. S. (2000) · 2000
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Local dimensionality reduction: A new approach to indexing high dimensional spaces
Chakrabarti, K. and Mehrotra, S. (2000) · 2000
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Approximate clustering via core-sets
Badoiu, M., Har-Peled, S., and Indyk, P. (2002) · 2002
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Projective clustering in high dimensions using core-sets
Har-Peled, S. and Varadarajan, K. R. (2002) · 2002
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A monte carlo algorithm for fast projective clustering
Procopiuc, C. M., Jones, M., Agarwal, P. K., and Murali, T. M. (2002) · 2002
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No, coreset, no cry
Har-Peled, S. (2004) · 2004
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On coresets for k-means and k-median clustering
Har-Peled, S. and Mazumdar, S. (2004) · 2004
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No coreset, no cry: II
Edwards, M. and Varadarajan, K. R. (2005) · 2005
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Coresets in dynamic geometric data streams
Frahling, G. and Sohler, C. (2005) · 2005
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Robust shape fitting via peeling and grating coresets
Agarwal, P. K., Har-Peled, S., and Yu, H. (2006) · 2006
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Matrix approximation and projective clustering via volume sampling
Deshpande, A., Rademacher, L., Vempala, S. S., and Wang, G. (2006) · 2006
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Coresets for weighted facilities and their applications
Feldman, D., Fiat, A., and Sharir, M. (2006) · 2006
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A guide to NumPy
Oliphant, T. E. (2006) · 2006
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Sampling-based dimension reduction for subspace approximation
Deshpande, A. and Varadarajan, K. R. (2007) · 2007
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On khachiyan’s algorithm for the computation of minimum-volume enclosing ellipsoids
Todd, M. J. and Yıldırım, E. A. (2007) · 2007
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Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Clarkson, K. L. (2008) · 2008
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Sampling algorithms and coresets for ℓ p \ell_{p} regression
Dasgupta, A., Drineas, P., Harb, B., Kumar, R., and Mahoney, M. W. (2008) · 2008
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A fast k-means implementation using coresets
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Aloise, D., Deshpande, A., Hansen, P., and Popat, P. (2009) · 2009
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Strong coresets for k-median and subspace approximation: Goodbye dimension
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Coresets meet EDCS: algorithms for matching and vertex cover on massive graphs
Assadi, S., Bateni, M., Bernstein, A., Mirrokni, V. S., and Stein, C. (2019) · 2019
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Improved algorithms for time decay streams
Braverman, V., Lang, H., Ullah, E., and Zhou, S. (2019) · 2019
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Dimensionality reduction for tukey regression
Clarkson, K., Wang, R., and Woodruff, D. (2019) · 2019
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Fast and accurate least-mean-squares solvers
Maalouf, A., Jubran, I., and Feldman, D. (2019) · 2019
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Python 3 Reference Manual
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Coresets and sketches for high dimensional subspace approximation problems
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Projective clustering
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A unified framework for approximating and clustering data
Feldman, D. and Langberg, M. (2011) · 2011
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Turning big data into tiny data: Constant-size coresets for k-means, pca, and projective clustering
Feldman, D., Schmidt, M., and Sohler, C. (2020) · 2020
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Coresets for clustering in euclidean spaces: importance sampling is nearly optimal
Huang, L. and Vishnoi, N. K. (2020) · 2020
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Sets clustering
Jubran, I., Tukan, M., Maalouf, A., and Feldman, D. (2020) · 2020
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Data-independent neural pruning via coresets
Mussay, B., Osadchy, M., Braverman, V., Zhou, S., and Feldman, D. (2020) · 2020
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Coresets for near-convex functions
Tukan, M., Maalouf, A., and Feldman, D. (2020) · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C., Polat, İ., Feng, Y., Moore, E. W., Vand erPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and Contributors (2020) · 2020
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Coresets for the average case error for finite query sets
Maalouf, A., Jubran, I., Tukan, M., and Feldman, D. (2021) · 2021
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Open source code for all the algorithms presented in this paper
Tukan, M., Wu, X., Zhou, S., Braverman, V., and Feldman, D. (2022) · 2022
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Tight sensitivity bounds for smaller coresets
Maalouf, A., Statman, A., and Feldman, D. (2020) · 2061
Closest in time.