1 - Multi-View Subspace Learning (3 papers) [Multi-view data]
Q. Yin, S. Wu, L. Wang, "Unified subspace learning for incomplete and unlabeled multi-view data", Pattern Recognition, Volume 67, pages 313-327, 2017.
Z. Ding, Y. Fu, “Robust Multi-View Subspace Learning through Dual Low-Rank Decompositions”, AAAI Conference on Artificial Intelligence, AAAI 2016, 2016.
M. White, Y. Yu, X. Zhang., D. Schuurmans, "Convex Multi-view Subspace Learning", NIPS 2012, 2012.
2 - Multilinear Subspace Learning (6 papers) [Multidimensional data]
M. Hirari, F. Centofanti, M. Hubert, S. Van Aelst “Multilinear Principal Component Analysis”, Preprint, March 2025.
H. Lu, K. Plataniotis, A. Venetsanopoulos, "Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data", CRC Press, Taylor and Francis, 2013.
H. Lu, K. Plataniotis, A. Venetsanopoulos,"A Survey of Multilinear Subspace Learning for Tensor Data", Pattern Recognition, Volume 44, No. 7, pages 1540-1551, July 2011.
H. Lu, K. Plataniotis, A. Venetsanopoulos, "Uncorrelated Multilinear Discriminant Analysis with Regularization and Aggregation for Tensor Object Recognition", IEEE Transactions on Neural Networks, Volume 20, No. 1, pages 103-123, January 2009.
H. Lu, K. Plataniotis, A. Venetsanopoulos, "Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning", IEEE Trans. on Neural Networks, Volume 20, No. 11, pages1820-1836, November 2009.
H. Lu, K. Plataniotis, A. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, volume 19, No. 1, pages 18-39, January 2008.
3 - Regularized Subspace Learning (3 papers) [Spectral regression]
D. Cai, X. He, and J. Han, "Spectral regression for efficient regularized subspace learning", IEEE International Conference on, Computer Vision, ICCV 2007, 2007.
B. Liu, S. Xia, F. Meng, Y. Zhou, "Extreme spectral regression for efficient regularized subspace learning" Neurocomputing, Volume 149, pages 171–179, 2015.
F. Meng, X. Yu, R. Xiang, Z. Zhang, “Quantum Algorithm for Spectral Regression for Regularized Subspace Learning”, IEEE Access, 2018.
4 - Graph Embedding (4 papers)
4.1 Matrix
C. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, S. Lin, "Graph embedding and extensions: a general framework for dimensionality reduction", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 29, No. 1, pages 40-51, 2007.
W. Yu, F. Nie, F. Wang, R. Wang, X. Li, "Fast and Flexible Large Graph Embedding based on Anchors “, IEEE Journal of Selected Topics in Signal Processing, December 2018.
N. Han, J. Wu, Y. Liang, X. Fang, W. Wong, S. Teng, “Low-rank and sparse embedding for dimensionality reduction”, Neural Networks pages 202–216, 2018.
4.2 Tensor
H. Liu, Z. Wang, F. Shang, Y. Shuyuan, S. Gou, L. Jiao, "Semi-supervised Tensorial Locally Linear Embedding for Feature Extraction using PolSAR Data”, IEEE Journal of Selected Topics in Signal Processing, December 2018.