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Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al.
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Better guarantees for k-means and Euclidean k-median by primal-dual algorithms
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Rohe, K., Chatterjee, S., and Yu, B · 2011
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Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown
Fishkind, D. E., Sussman, D. L., Tang, M., Vogelstein, J. T., and Priebe, C. E · 2013
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Matrix Computations
Golub, G. H. and Van Loan, C. F · 2013
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Constrained spectral clustering using L1 regularization
Kawale, J. and Boley, D · 2013
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Qin, T. and Rohe, K · 2013
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Ahmadian, S., Norouzi-Fard, A., Svensson, O., and Ward, J · 2017
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Fair clustering through fairlets
Chierichetti, F., Kumar, R., Lattanzi, S., and Vassilvitskii, S · 2017
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Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., and Raghavan, M · 2017
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Strong consistency of spectral clustering for stochastic block models
Su, L., Wang, W., and Zhang, Y · 2017
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Fairness constraints: Mechanisms for fair classification
Zafar, M. B., Valera, I., Rodriguez, M. G., and Gummadi, K. P · 2017
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Community detection and stochastic block models: Recent developments
Abbe, E · 2018
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Donini, M., Oneto, L., Ben-David, S., Shawe-Taylor, J., and Pontil, M · 2018
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Rösner, C. and Schmidt, M · 2018
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The price of fair PCA: One extra dimension
Samadi, S., Tantipongpipat, U., Morgenstern, J., Singh, M., and Vempala, S · 2018
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Fair coresets and streaming algorithms for fair k-means clustering
Schmidt, M., Schwiegelshohn, C., and Sohler, C · 2018
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Fair k k -center clustering for data summarization
Kleindessner, M., Awasthi, P., and Morgenstern, J · 2019
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