Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, and Aaron Sidford. Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. In 29th Annual Conference on Learning Theory, volume 49 of Proceedings of Machine Learning Research, pages 1147–1164, Columbia University, New York, New York, USA, 23–26 Jun 2016. PMLR.
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, and Michael I. Jordan. A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. arXiv preprint arXiv:1902.03736, 2019.
Richard A. Johnson and Dean W. Wichern. Applied Multivariate Statistical Analysis. Pearson, 6 edition, 2008.
Ian T. Jolliffe and Jorge Cadima. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 2016.
Mohammad Mahdi Kamani, Farzin Haddadpour, Rana Forsati, and Mehrdad Mahdavi. Efficient fair principal component analysis. Machine Learning, 111(10):3671-3702, 2022.
L Kirchner, J. Larson, S. Mattu, and J. Angwin. Machine bias. ProPublica, 2016.
René F. Kizilcec and Hansol Lee. Algorithmic Fairness in Education. arXiv preprint arXiv:2007.05443, 2021.
Matthaus Kleindessner, Samira Samadi, Pranjal Awasthi, and Jamie Morgenstern. Guarantees for Spectral Clustering with Fairness Constraints. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 3458-3467. PMLR, 09-15 Jun 2019.
Matthaus Kleindessner, Michele Donini, Chris Russell, and Muhammad Bilal Zafar. Efficient fair PCA for fair representation learning. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 of Proceedings of Machine Learning Research, pages 5250-5270. PMLR, 25-27 Apr 2023.
Jonas Moritz Kohler and Aurelien Lucchi. Sub-sampled Cubic Regularization for Non-convex Optimization. In Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 1895-1904. PMLR, 06-11 Aug 2017.
Syamantak Kumar and Purnamrita Sarkar. Streaming PCA for Markovian Data. arXiv preprint arXiv:2305.02456, 2023.
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi. Fairness without Demographics through Adversorially Reweighted Learning. In Advances in Neural Information Processing Systems, volume 33, pages 728-740. Curran Associates, Inc., 2020.
Junghyun Lee, Gwangsu Kim, Mahbod Olfat, Mark Hasegawa-Johnson, and Chang D. Yoo. Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 7363-7371, Jun. 2022.
Xun Li, Dongsheng Chen, Weipan Xu, Haohui Chen, Junjun Li, and Fan Mo. Explainable dimensionality reduction (XDR) to unbox AI 'black box' models: A study of AI perspectives on the ethnic styles of village dwellings. *Humanities and Social Sciences Communications*, 10(1):35, Jan 2023.
Xin Liang. On the optimality of the Oja's algorithm for online PCA. Statistics and Computing, 33(3): 62, Mar 2023.
Edo Liberty. Simple and Deterministic Matrix Sketching. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13, page 581-588, New York, NY, USA, 2013. Association for Computing Machinery.
Ziqi Liu, Yu-Xiang Wang, and Alexander Smola. Fast Differentially Private Matrix Factorization. In Proceedings of the 9th ACM Conference on Recommender Systems, RecSys '15, page 171-178, New York, NY, USA, 2015a. Association for Computing Machinery.