Conventional Streaming PCA (20 papers)
I. Mitliagkas, C. Caramanis, P. Jain, "Memory Limited, Streaming PCA", International Conference on Neural Information Processing Systems,NIPS 2013, Volume 2, pages 2886-2894, 2013.
I. Mitliagkas, C. Caramanis, P. Jain “Streaming PCA with Many Missing Entries”, KDD 2014, 2014.
P. Jain, C. Jin, S. Kakade, P. Netrapalli, A. Sidford, "Streaming PCA: Matching matrix Bernstein and near-optimal finite sample guarantees for Oja’s algorithm", International Conference on Learning Theory, pages 1147–1164, 2016.
Z. Zhu, Y. Li, "First Efficient Convergence for Streaming k-PCA: A Global, Gap-Free, and Near-Optimal Rate", Annual Symposium on Foundations of Computer Science, FOCS 2017, Berkeley, USA, pages 487-492, 2017.
P. Yang, C. Hsieh, J. Wang, "History PCA: A New Algorithm for Streaming PCA", Preprint, 2018.
T. Marinov, P. Mianjy, R. Arora, "Streaming Principal Component Analysis in Noisy Settings", Preprint 2018.
S. Alakkari, J. Dingliana, “An Acceleration Scheme for Memory Limited, Streaming PCA”, Preprint, 2018.
S. Alakkari, J. Dingliana, "An Acceleration Scheme for Mini-batch Streaming PCA", British Machine Vision Conference, BVMC 2019, 2019.
A. Henrikseny, R. Ward, "AdaOja: Adaptive Learning Rates for Streaming PCA.", Preprint, May 2019.
R. Arora, T. Marinov, "Efficient Convex Relaxations for Streaming PCA", Neural Information Processing Systems, NeurIPS 2019, 2019.
E. Amid, M. Warmuth, “An implicit form of Krasulina's k-PCA update without the orthonormality constraint”, AAAI Conference on Artificial Intelligence, 2020.
C. Chou, M. Wang, "ODE-Inspired Analysis for the Biological Version of Oja’s Rule in Solving Streaming PCA", Conference on Learning Theory, COLT 2020, Volume 125, pages 1339-1343, 2020.
Y. Wang, N. Klein, S. Morley, V. Jordanova, M. Henderson, A. Biswas, E. Lawrence, "TributaryPCA: Distributed, Streaming PCA for in Situ Dimension Reduction with Application to Space Weather Simulations", International Workshop on Data Analysis and Reduction for Big Scientific Data, Held in conjunction with SC 2021, DRBSD 2021, pages 33–39, 2021.
D. Fleisher, “Robust streaming PCA via percentile thresholding”, Master of Science, McGill University, 2021.
D. Huang, J. Weed, R. Ward “Streaming k-PCA: Efficient guarantees for Oja’s algorithm, beyond rank-one updates”, Conference on Learning Theory, 2021.
D. Bienstock, M. Jeong, A. Shukla, S. Yun, "Robust Streaming PCA", Neural Information Processing Systems, NeurIPS 2022, 2022.
S. Kumar, P. Sarkar, “Streaming PCA for Markovian Data”, Neural Information Processing Systems, NeurIPS 2023, 2023.
I. Diakonikolas, D. Kane, A. Pensia, T. Pitta, “Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA”, IEEE International Conference on Machine Learning, ICML 2023, 2023.
M. Zulqarnain, A. Gang, W. Bajwa, “C-DIEGO: An Algorithm with Near-Optimal Sample Complexity for Distributed, Streaming PCA”, Annual Conference on Information Sciences and Systems, CISS 2023, March 2023.
C. Lu, J. Zeng, Y Dong, X. Xu, "Streaming variational probabilistic principal component analysis for monitoring of nonstationary process", Journal of Process Control, Volume 133, January 2024.
Neural Networks (1 paper)
C. Axenie, R. Tudoran, S. Bortoli, M. Hassan, G. Brasche “NARPCA: Neural Accumulate-Retract PCA for Low-Latency High-Throughput Processing on Datastreams”, International Conference on Artificial Neural Networks, ICANN 2019, pages 253–266, 2019.