Generalized Low Rank Models |
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| Author:
| Udell, Madeleine
Horn, Corinne
Zadeh, Reza
Boyd, Stephen |
| Series title: | Foundations and Trends® in Machine Learning Ser. |
| ISBN: | 978-1-68083-140-5 |
| Publication Date: | Jun 2016 |
| Publisher: | Now Publishers
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| Book Format: | Paperback |
| List Price: | AUD $127.00 |
Book Description:
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Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Generalized Low Rank Models extends the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Generalized Low Rank Models extends the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.