Screen LibraryGeneralized low rank models /
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set 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-kn...
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| Main Authors: | |
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| Corporate Authors: | |
| Group Author: | ; ; |
| Published: |
Now Publishers,
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| Publisher Address: | [Hanover, Massachusetts] : |
| Publication Dates: | [2016] |
| Literature type: | Book |
| Language: | English |
| Series: |
Foundations and trends in machine learning,
volume 9, issue 1, pages 1-118 |
| Subjects: | |
| Summary: |
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set 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 e |
| Carrier Form: | xi, 129 pages : illustrations ; 24 cm. |
| Bibliography: | Includes bibliographical references (pages 117-129). |
| ISBN: |
9781680831405 1680831402 |
| Index Number: | QA278 |
| CLC: | O212.1 |
| Call Number: | O212.1/U19 |
| Contents: | 1. Introduction -- 1.1 Previous work -- 1.2 Organization. |