Feature Extraction for Signals and Images
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
The text discusses Nonnegative Matrix Factorization (NMF), a method employed for feature extraction in signals and images. NMF can also be applied to image compression by representing images as the product of nonnegative matrices. This technique aids in extracting significant features from the original signal or image while preserving essential information from the source data. In practice, NMF algorithms typically involve iterative optimization methods (such as multiplicative updates or gradient descent) to decompose the input matrix V into two nonnegative matrices W (basis features) and H (coefficients), where V ≈ W×H. The nonnegativity constraint leads to parts-based representations that often yield more interpretable features compared to other factorization methods. Consequently, Nonnegative Matrix Factorization holds substantial application value in the fields of signal processing and image analysis.
- Login to Download
- 1 Credits