Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm
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Prateek Jain | Sham M. Kakade | Praneeth Netrapalli | Aaron Sidford | Chi Jin | S. Kakade | Prateek Jain | Chi Jin | Praneeth Netrapalli | Aaron Sidford
[1] P. Wedin. Perturbation bounds in connection with singular value decomposition , 1972 .
[2] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[3] Ralph R. Martin,et al. Incremental Eigenanalysis for Classification , 1998, BMVC.
[4] I. Johnstone. On the distribution of the largest eigenvalue in principal components analysis , 2001 .
[5] Juyang Weng,et al. Candid Covariance-Free Incremental Principal Component Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[6] Manfred K. Warmuth,et al. Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension , 2006, NIPS.
[7] Ming-Hsuan Yang,et al. Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.
[8] David P. Woodruff,et al. Numerical linear algebra in the streaming model , 2009, STOC '09.
[9] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[10] Robert H. Halstead,et al. Matrix Computations , 2011, Encyclopedia of Parallel Computing.
[11] Joel A. Tropp,et al. User-Friendly Tail Bounds for Sums of Random Matrices , 2010, Found. Comput. Math..
[12] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[13] Huy L. Nguyen,et al. OSNAP: Faster Numerical Linear Algebra Algorithms via Sparser Subspace Embeddings , 2012, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.
[14] Edo Liberty,et al. Simple and deterministic matrix sketching , 2012, KDD.
[15] Sanjoy Dasgupta,et al. The Fast Convergence of Incremental PCA , 2013, NIPS.
[16] Ioannis Mitliagkas,et al. Memory Limited, Streaming PCA , 2013, NIPS.
[17] Moritz Hardt,et al. The Noisy Power Method: A Meta Algorithm with Applications , 2013, NIPS.
[18] Tengyu Ma,et al. Online Learning of Eigenvectors , 2015, ICML.
[19] Ohad Shamir,et al. A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate , 2014, ICML.
[20] Sham M. Kakade,et al. Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation , 2015, ArXiv.
[21] Christopher De Sa,et al. Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems , 2014, ICML.
[22] Christos Boutsidis,et al. Online Principal Components Analysis , 2015, SODA.
[23] Elad Hazan,et al. Fast and Simple PCA via Convex Optimization , 2015, ArXiv.
[24] Jakub W. Pachocki,et al. Geometric median in nearly linear time , 2016, STOC.
[25] David P. Woodruff,et al. Frequent Directions: Simple and Deterministic Matrix Sketching , 2015, SIAM J. Comput..
[26] David P. Woodruff,et al. Optimal Approximate Matrix Product in Terms of Stable Rank , 2015, ICALP.
[27] Ohad Shamir,et al. Convergence of Stochastic Gradient Descent for PCA , 2015, ICML.