Publications

PCA (43 papers)

1. Regular PCA (7 papers)

Gaussian PCA

K. Pearson, "On lines and planes of closest fit to systems of points in space", Philosophical Magazine, Volume 2, No. 11, pages 559-572, 1901.

H. Hotelling, "Analysis of a Complex of Statistical Variables Into Principal Components, Journal of Educational Psychology, Volume 24, pages 417-441, 1933.

I. Jollife, "Principal Component Analysis", New York: Springer-Verlag, 2002.

MultiVariate Student-t PCA (MV Student-t PCA)

Z. Khan, F. Dellaert, "Robust generative subspace modeling: The subspace t distribution", 2004.

Laplacian PCA (LPCA)

D. Zhao, Z. Lin, X. Tang, "Laplacian PCA and Its Applications", IEEE International Conference on Computer Vision, ICCV 2007, 2007.

Cauchy (CPCA)

P. Xie, E. Xing, “Cauchy Principal Component Analysis”, International Conference on Learning Representations, ICLR 2015, San Diego, USA, May 2015.

Coupled PCA

R. Moller, A. Konies, "Coupled Principal Component Analysis", IEEE Transactions on Neural Networks, Volume 15, No. 1, pages 214-222, 2004.

2. Correlated PCA (7 papers)

2.1 Dynamic PCA (4 papers) [Chemometrics]

2.2 Correlated PCA (3 papers)

3. Adaptive PCA (9 papers)

3.1 Recursive PCA (5 papers) [Chemometrics]

3.2 Moving Window PCA (4 papers) [Chemometrics]

4 - Streaming PCA/Subspace Tracking (20 papers)

4.1 Streaming PCA (3 papers)

4.2 Subspace Tracking (15 papers) [Computer data]

4.3 Incremental PCA (2 papers)

Classical Robust PCA (105 papers) [Low-dimensions]

1. - Classical RPCA in Statistics (85 papers) [Basic outliers]

2. - Classical RPCA in Neural Networks (11 papers) [Basic outliers]

3 - Classical RPCA/RMC in Statistics (2 papers) [Missing data]

4. Classical RPCA with Fuzzy Concepts (7 papers)

Robust PCA via L+S decomposition [Spike outliers] [high dimensions]

Please see DLAM website for the full list of publications [DLAM Website].

1. Principal Component Pursuit (3 papers) [Element-wise outliers]

RPCA

E. Candes, X. Li, Y. Ma, J. Wright, “Robust Principal Component Analysis”, ACM, Volume 58, No. 3, May 2011.

Stable RPCA

Z. Zhou, X. Li, J. Wright, E. Candes, Y. Ma, "Stable Principal Component Pursuit", ISIT 2010, 2010.

Dynamic RPCA

C. Qiu, N. Vaswani, “Real-time Robust Principal Components Pursuit”, International Conference on Communication Control and Computing, 2010.

2. Robust Subspace Recovery (3 papers) [Row-wise or column-wise outliers]

H. Xu, C. Caramanis, S. Sanghavi, “Robust PCA via Outlier Pursuit”, International Conference on Neural Information Processing System, NIPS 2010, 2010.

H. Zhang, Z. Lin, C. Zhang, E. Chan, "Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds", Advancement of Artificial Intelligence, AAI 2015, 2015.

M. Rahmani, G. Atia, "Robust PCA with concurrent column and element-wise outliers", Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, pages 332-337, 2017.

3. Robust Matrix Completion (9 papers) [Missing data]

Y. Yang, Y. Feng, J. Suykens, “A Nonconvex Relaxation Approach to Robust Matrix Completion”, Preprint, 2014.

Y. Yang, Y. Feng, J. Suykens, “Correntropy based Matrix Completion”, MDPI Entropy, Volume 20, No. 3, page 171, 2018.

F. Shang, Y. Liu, J. Cheng, H. Cheng, "Recovering low-rank and sparse matrices via robust bilateral factorization", IEEE International Conference on Data Mining, ICDM 2014, 2014.

F. Shang, Y. Liu, H. Tong, J. Cheng, H. Cheng, "Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations", Preprint, September 2014.

F. Shang, Y. Liu, J. Cheng, H. Cheng, "Robust Principal Component Analysis with Missing Data", ACM International Conference on Information and Knowledge Management, CIKM 2014, 2014.

S. Wang, D. Liu, Z. Zhan, "Nonconvex Relaxation Approaches to Robust Matrix Recovery", International Joint Conference on Artificial Intelligence, 2013.

T. Yokota, A. Cichocki, “A fast automatic low-rank determination algorithm for noisy matrix completion”, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2015, pages 43-46, Hong Kong, 2015.

Y. Cherapanamjeri, K. Gupta, P. Jain, "Nearly-optimal Robust Matrix Completion", Preprint, December 2016.

M. Ashraphijuo, V. Aggarwal, X. Wang, "On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion" , IEEE Signal Processing Letters, Volume 25, no. 3, pages 343-347, March 2018.

Scalable RPCA (4 papers)

PCA

T. Elgamal, M. Yabandeh, A. Aboulnaga, M. Hefeeda, "sPCA: Scalable Principal Component Analysis for Big Data on Distributed Platform", March 2015.

RPCA

S. Hauberg, A. Feragen, M. Black, “Grassmann Averages for Scalable Robust PCA”, RGA 2014, 2014.

S. Hauberg, A. Feragen, R. Enficiaud, M. Black, "Scalable robust principal component analysis using Grassmann averages", IEEETrasanctions on Pattern Analysis and Machine Intelligence, 2015.

R. Chakraborty, S. Hauberg, B. Vemuri, “Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning”, CVPR 2017, 2017.

Stochastic PCA (3 papers)

R. Arora, A. Cotter, K. Livescu, N. Srebro, "Stochastic optimization for PCA and PLS", Allerton Conference, pages 851-868, 2012.

R. Arora, A. Cotter, N. Srebro, "Stochastic minimization of PCA with capped MSG" , Neural Information Processing Systems, 2013.

J. Goes, T. Zhang, R. Arora, G. Lerman, "Robust stochastic principal component analysis", International Conference on Artificial Intelligence and Statistics, AISTATS 2014, April 2014.

Sparse PCA (6 papers)

H. Zou, T. Hastie, R. Tibshirani, "Sparse principal component analysis", Journal of Computational and Graphical Statistics, Volume 15, pages 262-266, 2006.

I. Johnstone, A. Lu, "On Consistency and Sparsity for Principal Components Analysis in High Dimensions", Journal of the American Statistical Association, pages 682–693, 2009.

W. Liu, H. Zhang, D. Tao, Y. Wang, K. Lu, "Large-scale paralleled sparse principal component analysis", Multimedia Tools and Applications, pages 1-13, 2014.

M. Hubert, T. Reynkens, E. Schmit, "Sparse PCA for high-dimensional data with outliers", Technometrics, 2016.

Robust Sparse PCA

W. Ling, J. Yin, "The Robust Sparse PCA for Data Reconstructive via Weighted Elastic Net", International Conference on Communications, Signal Processing, and Systems, pages 225-234, 2012.

Q. Zhao, D. Meng, Z. Xu, "Robust sparse principal component analysis", Information Sciences, Volume 57, pages 1-14, September 2014

Probabilistic PCA (9 papers)

PPCA [Gaussian]

M. Tipping, C. Bishop,"Probabilistic Principal Component Analysis”, Journal of the Royal Statistical Society, Series B, Volume 61, No. 3, pages 611-622, September 1999.

M. Tipping, C. Bishop, "Mixtures of probabilistic principal component analysers”, Neural Computation, Volume 11, No. 2, pages 443–482, February 1999.

PPCA [Exponential Family]

M. Collins, S. Dasgupta, R. Schapire, “A Generalization of Principal Component Analysis to the Exponential Family,” Advances on Neural Information Processing Systems, Volume 14, 2001.

Supervised PPCA (SPPCA)

S. Yu, K. Yu, V. Tresp, H. Kriege, M. Wu, "Supervised Probabilistic Principal Component Analysis," 2002.

Hierarchial PPCA (HPPCA)

T. Su, J. Dy, "Automated Hierarchical Mixtures of Probabilistic Principal Component Analyzers", International Conference on Machine Learning, ICML 2014, 2014

Sparse PPCA

T. Su, J. Dy, "Sparse Probabilistic Principal Component Analysis", International Conference on Artificial Intelligence and Statistics, AISTATS 2009, 2009.

Improved PPCA

I. Udagedara, B. Helenbrook, A. Luttman, J. Catenacci, "Improved Probabilistic Principal Component Analysis for Application to Reduced Order Modeling", Preprint, 2017.

Probabilistic Non-linear PCA

N. Lawrence, "Probabilistic Non-linear Principal Component Analysis withGaussian Process Latent Variable Model", Journal of Machine Learning Research, Volume 5, pages 1783–1816, 2005.

Fuzzy Probabilistic PCA

K. Honda, H. Ichihashi, “Regularized Linear Fuzzy Clustering and Probabilistic PCA Mixture Models”, IEEE Transactions on Fuzzy Systems, 2005.

Distributed PCA (6 papers)

Z. Bai, R. Chan, F. Lu, "Principal Component Analysis for Distributed Data Sets with Updating" International Workshop on Advanced Parallel Processing Technologies, pages 471-483, 2005.

A. Aduroja, I. Schizas, V. Maroulas, "Distributed principal components analysis in sensor networks", IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, pages 5850-5854, 2013.

M. Balcan, V. Kanchanapally, Y. Liang, D. Woodruff, "Improved Distributed Principal Component Analysis", Advances in neural information processing systems, 2014.

T. Elgamal, M. Hefeeda, "Analysis of PCA Algorithms in Distributed Environments", 2015.

J. Zhu, Z. Ge, Z. Song, "Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes with Big Data", IEEE Transactions on Industrial Informatics, Volume 13, No. 4, pages 1877-1885, 2017.

V. Huroyan, G. Lerman, "Distributed robust subspace recovery", March 2018.

Kernel PCA (7 papers)

Kernel PCA (KPCA)

B. Scholkopf, A. Smola, K. Muller,“Kernel Principal Component Analysis”, International Conference on Artificial Neural Networks, ICANN 1997, pages 583-588, 1997.

Iterative KPCA [Hebbian Kernel]

K. Kim, M. Franz, B. Scholkopf, “Iterative kernel principal component analysis for image modeling", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27, Issue 9, pages 1351-1366, 2005.

S. Gunter, N. Schraudolph, S. Vishwanathan “Fast Iterative Kernel Principal Component Analysis”, Journal of Machine Learning Research, pages 1893-1918, 2007.

Incremental KPCA

T. Chin, D. Suter, "Incremental Kernel Principal Component Analysis", IEEE Transactions on Image Processing, Volume.16, No. 6, pages 1662-1674, JUNE 2007

RKPCA

M. Nguyen, F. Torre, "Robust Kernel Principal Component Analysis", NIPS 2008, 2008.

E-PCA [Euler Kernel]

S. Liwick, G. Tzimiropoulos, S. Zafeiriou, M. Pantic, "Euler principal component analysis", International Journal on Computer Vision, Volume 101, Issue 3,pages 498–518, February 2013.

Streaming KPCA

M. Ghasham, D. Perry, M. Phillips, "Streaming Kernel Principal Component Analysis", International Conference on Artificial Intelligence and Statistics, AISTATS 2016, 2016.

Spherical PCA (2 papers)

Spherical PCA

J. Fujiki, S. Akaho, "Spherical PCA with Euclideanization", Workshop on Subspace in conjunction with ACCV 2007, 2007.

Spherical Kernel PCA

M. Debruyne, M. Hubert, J. Van Horebeek, “Detecting influential observations in kernel PCA”, Computational Statistics and Data Analysis, Volume 54, pages 3007–3019, 2010.

Local/Global PCA (3 papers)

Locally Weighted PCA (LWPCA)

M. Charlton, C. Brunsdon, U. Demsar, P. Harris, A. Stewart, “Principal Components Analysis: from Global to Local”, International Conference on Geographic Information Science, 2010.

LGPCA

J. Yu, “Local and global principal component analysis for process monitoring”, Journal of Process Control, Volume 22, Issue 7, pages 1358-1373, 2012.

Global PCA

H. Qi, T. Wang, J. Birdwell, “Global Principal Component Analysis for Dimensionality Reduction in Distributed Data Mining”.

Modular PCA (2 papers)

Modular PCA (ModPCA)

R. Gottumukkal, V. Asari, “An improved face recognition technique based on modular PCA approach” Pattern Recognition Letters, pages 429-436, 2004.

Global (GModPCA)

V. Kadappa, A. Negi, “Global Modular Principal Component Analysis”, Signal Processing, Volume 105, pages 381-388, December 2014.

Generalized PCA (1 paper)

R. Vidal, Y. Ma, S. Sastry, "Generalized Principal Component Analysis (GPCA)", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27, No. 12, pages 1945-1959, December 2005.