Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization
β Scribed by Yokoya, Naoto; Chanussot, Jocelyn; Iwasaki, Akira
- Book ID
- 121652912
- Publisher
- IEEE
- Year
- 2014
- Tongue
- English
- Weight
- 829 KB
- Volume
- 52
- Category
- Article
- ISSN
- 0196-2892
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π SIMILAR VOLUMES
Nonnegative matrix factorization has been offered as a fast and effective method for analyzing nonnegative two-mode proximity data. The goal is to structurally represent a nonnegative proximity matrix as the product of two lower-dimensional nonnegative matrices. Goodness of fit is typically measured
This book constitutes the proceedings of the 5th International Conference on Nonlinear Speech Processing, NoLISP 2011, held in Las Palmas de Gran Canaria, Spain, in November 2011. The purpose of the workshop is to present and discuss new ideas, techniques and results related to alternative approache