Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., Hengel, A. v. d., Zhang, K., and Shi, J. Q. (2022). Identifying weight-variant latent causal models. arXiv preprint arXiv:2208.14153.
Lopez, R., Tagasovska, N., Ra, S., Cho, K., Pritchard, J., and Regev, A. (2022). Learning causal representations of single cells via sparse mechanism shift modeling. In NeurIPS 2022 Workshop on Causality for Real-world Impact.
Lukic, A. S., Wernick, M. N., Hansen, L. K., and Strother, S. C. (2002). An ICA algorithm for analyzing multiple data sets. In Proceedings. International Conference on Image Processing, volume 2, pages 821-824. IEEE.
Lyche, T. (2020). Numerical linear algebra and matrix factorizations, volume 22 of Texts in Computational Science and Engineering. Springer, Cham. With a foreword by Geir Dahl.
Maneshi, M., Vahdat, S., Gotman, J., and Grova, C. (2016). Validation of shared and specific independent component analysis (SSICA) for between-group comparisons in fMRI. Front. Neurosci., 10:417.
McQuin, C., Goodman, A., Chernyshev, V., Kamentsky, L., Cimini, B. A., Karhohs, K. W., Doan, M., Ding, L., Rafelski, S. M., Thirstrup, D., Wiegraeb, W., Singh, S., Becker, T., Caicedo, J. C., and Carpenter, A. E. (2018). CellProfiler 3.0: Next-generation image processing for biology. PLOS Biol., 16(7):e2005970.
Mesters, G. and Zwiernik, P. (2022). Non-independent components analysis. arXiv preprint arXiv:2206.13668.
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., Griffanti, L., Douaud, G., Okell, T. W., Weale, P., Dragonu, I., Garratt, S., Hudson, S., Collins, R., Jenkinson, M., Matthews, P. M., and Smith, S. M. (2016). Multimodal population brain imaging in the UK biobank prospective epidemiological study. Nat. Neurosci., 19(11):1523-1536.
Nielsen, A. A. (2002). Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data. IEEE Trans. Image Process., 11(3):293-305.
Pandeva, T. and Forre, P. (2023). Multi-view independent component analysis with shared and individual sources. In Evans, R. J. and Shpitser, I., editors, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, volume 216 of Proceedings of Machine Learning Research, pages 1639-1650. PMLR.
Richard, H., Ablin, P., Thirion, B., Gramfort, A., and Hyvarinen, A. (2021). Shared independent component analysis for multi-subject neuroimaging. Adv. Neural Inf. Process. Syst., 34:29962-29971.
Roeder, G., Metz, L., and Kingma, D. (2021). On linear identifiability of learned representations. In Meila, M. and Zhang, T., editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 9030-9039. PMLR.
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y. (2021). Toward causal representation learning. Proc. IEEE, 109(5):612-634.
Shafto, M. A., Tyler, L. K., Dixon, M., Taylor, J. R., Rowe, J. B., Cusack, R., Calder, A. J., Marslen-Wilson, W. D., Duncan, J., Dalgleish, T., Henson, R. N., Brayne, C., and Matthews, F. E. (2014). The cambridge centre for ageing and neuroscience (cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol., 14(1).
Shimizu, S., Inazumi, T., Sogawa, Y., Hyvarinen, A., Kawahara, Y., Washio, T., Hoyer, P. O., and Bollen, K. (2011). DirectLiNGAM: a direct method for learning a linear non-Gaussian structural equation model. J. Mach. Learn. Res., 12:1225-1248.
Silva, R., Scheine, R., Glymour, C., and Spirtes, P. (2006). Learning the structure of linear latent variable models. J. Mach. Learn. Res., 7(8):191-246.
Squires, C., Seigal, A., Bhate, S. S., and Uhler, C. (2023). Linear causal disentanglement via interventions. In Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., and Scarlett, J., editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 32540-32560. PMLR.
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., and Satija, R. (2019). Comprehensive integration of single-cell data. Cell, 177(7):1888-1902.
Sui, J., Adali, T., Pearson, G. D., and Calhoun, V. D. (2009). An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage, 46(1):73-86.
Svensén, M., Kruggel, F., and Benali, H. (2002). ICA of fMRI group study data. Neuroimage, 16(3):551-563.