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CMI Seminar

Tuesday, February 24, 2015
4:00pm to 5:00pm
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Annenberg 213
Generalized Low Rank Models
Madeleine Udell, Computational & Mathematical Engineering, Stanford University,

Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features.     

We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.

For more information, please contact Linda Taddeo by phone at 626-395-6704 or by email at [email protected] or visit Mathematics of Information Seminar - Upcoming Events.