[5] Yijuan Lu, Ira Cohen, Xiang Sean Zhou, and Qi Tian. Feature selection using principal feature analysis. In Proceedings of the 15th ACM International Conference on Multimedia, pages 301-304, 2007.
[6] Jennifer G Dy and Carla E Brodley. Feature selection for unsupervised learning. Journal of Machine Learning Research, 5(Aug):845-889, 2004.
[7] Deng Cai, Chiyuan Zhang, and Xiaofei He. Unsupervised feature selection for multi-cluster data. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 333-342, 2010.
[8] Ahmed K Farahat, Ali Ghodsi, and Mohamed S Kamel. An efficient greedy method for unsupervised feature selection. In 2011 IEEE 11th International Conference on Data Mining, pages 161-170. IEEE, 2011.
[9] James Yang Zou, Daniel Hsu, David C Parkes, and Ryan Prescott Adams. Contrastive learning using spectral methods. Proceedings of Advances in Neural Information Processing Systems, 2013.
[10] Abubakar Abid, Martin J Zhang, Vivek K Bagaria, and James Zou. Exploring patterns enriched in a dataset with contrastive principal component analysis. Nature Communications, 9(1):1-7, 2018.
[11] Abubakar Abid and James Zou. Contrastive variational autoencoder enhances salient features. arXiv preprint arXiv:1902.04601, 2019.
[12] Giorgio Stanta and Serena Bonin. Overview on clinical relevance of intra-tumor heterogeneity. Frontiers in Medicine, 5:85, 2018.
[13] Ethan Weinberger, Chris Lin, and Su-In Lee. Isolating salient variations of interest in single-cell data with contrastivevi. Nature Methods, pages 1-10, 2023.
[14] Efthymia Papalexi, Eleni P Mimitou, Andrew W Butler, Samantha Foster, Bernadette Bracken, William M Mauck III, Hans-Hermann Wessels, Yuhan Hao, Bertrand Z Yeung, Peter Smibert, et al. Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens. Nature Genetics, 53(3):322-331, 2021.
[15] Michael S Kinch. An analysis of FDA-approved drugs for oncology. *Drug Discovery Today*, 19 (12):1831–1835, 2014.
[16] Kristen A Severson, Soumya Ghosh, and Kenney Ng. Unsupervised learning with contrastive latent variable models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4862-4869, 2019.
[17] Adrià Ruiz, Oriol Martinez, Xavier Binefa, and Jakob Verbeek. Learning disentangled representations with reference-based variational autoencoders. arXiv preprint arXiv:1901.08534, 2019.
[18] Didong Li, Andrew Jones, and Barbara Engelhardt. Probabilistic contrastive principal component analysis. arXiv preprint arXiv:2012.07977, 2020.
[19] Andrew Jones, F William Townes, Didong Li, and Barbara E Engelhardt. Contrastive latent variable modeling with application to case-control sequencing experiments. arXiv preprint arXiv:2102.06731, 2021.
[20] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, pages 1597-1607. PMLR, 2020.
[21] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021.