High dimensional geometry and Data analysis
I gave a series of lectures under the above title at VIASM this summer. Here are some literature related to these lectures:
Concentration of measure:
(1) concentration of measure for the analysis of randomized algorithms, Dubhashi and Panconesi.
(2) Concentration of non-Liipschitz functions, Van Vu (RSA journal).
(3) Concentration of measure, Ledoux.
Norm of random matrices:
(1) Spectral norm of random matrices, Van Vu (Combinatorica).
(2) Introduction to the non-asymptotics analsis of random matrices, lecture notes of R. Vershynin from his website at UMichigan.
(3) Random matrices, Bai and Silverstein.
(4) Random matrices, Tao.
(1) Achlioptas, Dimitris Database-friendly random projections: Johnson-Lindenstrauss with binary coins + references in there.
(1) Random weighted projections, random quadratic forms, and random eigenvectors,
(1) A simple SVD algorithm for finding hidden partitions (and the references in therere): Va Vu, http://arxiv-web3.library.cornell.edu/abs/1404.3918.
(2) Compress sensing, theory and applications, CUP, Edited by Eldar et. al.