Nonnegative matrix factorization
Series: Data science ; 2Publication details: Society for Industrial and Applied Mathematics, 2021 Philadelphia :Description: xxv, 350 p. ; ill. 26 cmISBN:- 9781611976403
- Algorithms
- Data Science Data Analytics
- Computer algorithms
- Data mining
- Non-negative matrices
- Bolzano-Weierstress theorem
- Condition number
- Contact change point
- Distributional robustness
- Feature extraction
- Hierarchical alternating least squares
- Identifiability
- Karush-Kuhn-Tucker (KKT) conditions
- MATLAB
- Minimum -volumeNMF
- Near-separable factorization
- Nested polytope problem
- Nonnegative rank
- Orthogonal NMF
- Principal component analysis
- Robust PCA
- Thomas matrix
- Verttex component analysis
- High dimensional data sets
- 512.9434 GIL
| Cover image | Item type | Current library | Home library | Collection | Shelving location | Call number | Materials specified | Vol info | URL | Copy number | Status | Notes | Date due | Barcode | Item holds | Item hold queue priority | Course reserves | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Books | DAU | 512.9434 GIL | Available | 033451 |
Includes bibliographical references and index.
This book provides a comprehensive and up-to-date account of the NMF problem and its most significant features.
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