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LEARNINGDimensionality Reduction for Matrix- and Tensor-Coded Data [Part 1] (53:36)
- 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.
The first half of the tutorial will cover theoretical concepts and foundations of these methods, many of which are surprisingly recent results. The second half will include hands-on exercises and advice for fitting these models in practice.
Additional Tutorial information and materials can be found Stellar here - https://stellar.mit.edu/S/project/bcs-comp-tut/materials.html