Feature extraction
Implementations
Many data analysis software packages provide for feature extraction and dimension reduction. Common numerical programming environments such as MATLAB, SciLab, NumPy, scikit-learn and the R language provide some of the simpler feature extraction techniques (e.g. principal component analysis) via built-in commands. More specific algorithms are often available as publicly available scripts or third-party add-ons. There are also software packages targeting specific software machine learning applications that specialize in feature extraction.[1]
See also
- Cluster analysis
- Dimensionality reduction
- Feature detection
- Feature selection
- Data mining
- Connected-component labeling
- Segmentation (image processing)
- Space mapping
- Dynamic texture
- Radiomics
References
- ^ See, for example, https://reality.ai/
 | This article needs additional citations for verification. (January 2016) |
Rustum, Rabee, Adebayo Adeloye, and Aurore Simala. "Kohonen self-organising map (KSOM) extracted features for enhancing MLP-ANN prediction models of BOD5." In International Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management-24th General Assembly of the International Union of Geodesy and Geophysics (IUGG), pp. 181-187. 2007.