Dimensionality Reduction for Matrix- and Tensor-Coded Data (1 & 2)

Taught by: Alex Williams, Stanford University (September 5, 2017)


Description: 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 include 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 the 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.


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Course Info

As Taught In
Fall 2023
Learning Resource Types
Lecture Videos
Tutorial Videos