Dimensionality Reduction for Matrix- and Tensor-Coded Data [Part 2] (46:05)
- Computational Tutorials
Taught by: Alex Williams, Stanford University
In many scientific domains, data is coded in large tables or higher-dimensional arrays. Compressing these data into smaller, more manageable representations is often critical for extracting scientific insights. This tutorial covers matrix and tensor factorizations - a large class of dimensionality-reduction methods that includes PCA, non-negative matrix factorization (NMF), independent components analysis (ICA), and others. We pay special attention to canonical polyadic (CP) tensor decomposition, which extends PCA to higher-order data arrays. The first half of the tutorial covers theoretical concepts and foundations of these methods, many of which are surprisingly recent results. The second half includes hands-on exercises and advice for fitting these models in practice.
After the tutorial, slides and resources will be posted on the computational tutorials stellar page.
slides, references, and exercises: https://stellar.mit.edu/S/project/bcs-comp-tut/materials.html
videos: http://cbmm.mit.edu/videos?field_video_grouping_tid[0]=781