Unsupervised Feature Learning with Matrix Decomposition - Aedin Culhane, PhD | ODSC East 2018

Explore unsupervised feature learning techniques using matrix decomposition in this comprehensive conference talk from ODSC East 2018. Delve into the world of unsupervised learning algorithms, focusing on dimension reduction and matrix factorization approaches for analyzing high-dimensional data without labeled classes. Gain insights into various matrix factorization techniques, including principal component analysis, correspondence analysis, non-negative matrix factorization, t-SNE, and autoencoders. Learn about extensions of these methods for simultaneous analysis of multiple datasets, such as canonical correlations analysis and multiple factor analysis. Discover how these approaches are applied to analyze tens of thousands of tumors, advancing precision medicine in oncology. The talk covers topics ranging from cancer microenvironment analysis to single-cell data analysis pipelines, classical dimension reduction techniques, and the integration of multiple datasets using advanced methods like multi-CIA and moGSA. Understand the application of these techniques in finding PanCancer Immune subtypes and exploring correlations between clusters, leucocyte fraction, and mutation load.