Covariance discriminative power of kernel clustering methods
Author:
Affiliation:
1. King Abdullah University of Science and Technology (Saudi Arabia)
2. CentraleSupélec – LSS – Université ParisSud (Gif-sur-Yvette, France)
Publisher
Institute of Mathematical Statistics
Subject
Statistics and Probability,Statistics, Probability and Uncertainty
Reference20 articles.
1. Ali, H. T., Kammoun, A., and Couillet, R. (2018). Random matrix asymptotics of inner product kernel spectral clustering. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2441–2445.
2. Bai, Z. D. and Silverstein, J. W. (1998). No Eigenvalues Outside the Support of the Limiting Spectral Distribution of Large Dimensional Sample Covariance Matrices. Annals of Probability, 26(1):316–345.
3. Bai, Z. D. and Silverstein, J. W. (2006). Spectral Analysis of Large Dimensional Random Matrices. Mathematics Monograph Series 2. Science Press, Beijing.
4. Capitaine, M., Martin, C. D., and Féral, D. (2009). “The Largest Eigenvalues of Finite Rank Deformation of Large Wigner Matrices: Convergence and Nonuniversality of the Fluctuations”. The Annals of Probability, 37(1):1–47.
5. Cheng, X. and Singer, A. (2013). The spectrum of random inner-product kernel matrices. Random Matrices: Theory and Applications, 02(04):1350010.
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