Page 118 - AIH-1-3
P. 118
Artificial Intelligence in Health ISM: A new multi-view space-learning model
11. Yang M, Hussain I. Unsupervised multi-view k-means 2024;217:109341.
clustering algorithm. IEEE Access. 2023;11:13574-13593.
doi: 10.1016/j.sigpro.2023.109341
doi: 10.1109/access.2023.3243133
24. Fu L, Lin P, Vasilakos AV, Wang S. An overview of recent
12. Hussain I, Sinaga KP, Yang M. Unsupervised multiview multi-view clustering. Neurocomputing. 2020;402:148-161.
fuzzy C-means clustering algorithm. Electronics. doi: 10.1016/j.neucom.2020.02.104
2023;12(21):4467-4467.
25. Dua D, Graff C. UCI Machine Learning ℝepository. Irvine,
doi: 10.3390/electronics12214467
CA: University of California, School of Information and
13. Smilde AK, Westerhuis JA, de Jong S. A framework for Computer Science; 2017. Available from: https://archive.ics.
sequential multiblock component methods. J Chemometr. uci.edu/dataset/72/multiple+features
2003;17(6):323-337.
26. Boldina G, Fogel P, Rocher C, Bettembourg C, Luta G,
doi: 10.1002/cem.811 Augé F. A2Sign: Agnostic algorithms for signatures-a
14. Trendafilov NT. Stepwise estimation of common principal universal method for identifying molecular signatures from
components. Comput Stat Data Anal. 2010;54(12):3446-3457. transcriptomic datasets prior to cell-type deconvolution.
Bioinformatics. 2021;38(4):1015-1021.
doi: 10.1016/j.csda.2010.03.010
doi: 10.1093/bioinformatics/btab773
15. Tenenhaus A, Tenenhaus M. Regularized generalized
canonical correlation analysis for multiblock or multigroup 27. Lewis DD, Yang Y, Rose TG, Li F. RCV1: A new benchmark
data analysis. Eur J Oper Res. 2014;238(2):391-403. collection for text categorization research. J Mach Learn ℝes.
2004;5:361-397.
doi: 10.1016/j.ejor.2014.01.008
28. Brbic M, Piškorec M, Vidulin V, Kriško A, Šmuc T, Supek F.
16. Zhang C, Hu Q, Fu H, Zhu PF, Cao X. Latent Multi-View The landscape of microbial phenotypic traits and associated
Subspace Clustering. In: IEEE Conference on Computer genes. Nucleic Acids ℝes. 2016;44:10074-10090.
Vision and Pattern ℝecognition (CVPℝ); 2017. p. 4333-4341.
doi: 10.1093/nar/gkw964
doi: 10.1109/cvpr.2017.461
29. Swanson E, Lord C, Reading J, et al. Simultaneous trimodal
17. Chen M, Huang L, Wang C, Huang D. Multi-view clustering single-cell measurement of transcripts, epitopes, and
in latent embedding space. Proc AAAI Conf Artif Intell. chromatin accessibility using TEA-seq. Elife. 2021;10:e63632.
2020;34(4):3513-3520.
doi: 10.7554/eLife.63632
doi: 10.1609/aaai.v34i04.5756
30. Hirschman AO. The paternity of an index. Am Econ ℝev.
18. Leppäaho E, Ammad-ud-din M, Kaski S. GFA: Exploratory 1964;54:761-762.
analysis of multiple data sources with group factor analysis.
J Mach Learn ℝes. 2017;18(1):1294-1298. 31. Fogel P, Geissler C, Morizet N, Luta G. On rank selection
in non-negative matrix factorization using concordance.
19. Zhao S, Gao C, Mukherjee S, Engelhardt BE. Bayesian group Mathematics. 2023;11(22):4611.
factor analysis with structured sparsity. J Mach Learn ℝes.
2016;17(1):6868-6914. doi: 10.3390/math11224611
20. Zhang X, Zhao L, Zong L, Liu X, Yu H. Multi-view 32. Badeau R, Bertin N, Vincent E. Stability analysis of
Clustering via Multi-Manifold Regularized Nonnegative multiplicative update algorithms and application to
Matrix Factorization. In: IEEE International Conference on nonnegative matrix factorization. IEEE Trans Neural Netw.
Data Mining; 2014. p. 1103-1108. 2010;21(12):1869-1881.
doi: 10.1109/icdm.2014.19 doi: 10.1109/tnn.2010.2076831
21. Huizing G, Deutschmann IM, Peyré G, Cantini L. Paired 33. Donoho DL, Stodden V. When does non-negative matrix
single-cell multi-omics data integration with Mowgli. Nat factorization give a correct decomposition into parts? Adv
Commun. 2023;14(1):7711. Neural Inf Process Syst. 2003;16:1141-1148.
doi: 10.1038/s41467-023-43019-2 doi: 10.7916/d88d05n7
22. Brbic M, Kopriva I. Multi-view low-rank sparse subspace 34. Hubert L, Arabie P. Comparing partitions. J Classif.
clustering. Pattern ℝecognit. 2018;73:247-258. 1985;2(1):193-218.
doi: 10.1016/j.patcog.2017.08.024 doi: 10.1007/BF01908075
23. Dong Y, Che H, Leung MF, Liu C, Yan Z. Centric graph 35. Strehl A, Ghosh J. Cluster ensembles-A knowledge reuse
regularized log-norm sparse non-negative matrix framework for combining multiple partitions. J Mach Learn
factorization for multi-view clustering. Signal Process. ℝes. 2002;3:583-617.
Volume 1 Issue 3 (2024) 112 doi: 10.36922/aih.3427

