General
Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. When performing analysis of complex data one of the major problems stems from the number of variables involved. Analysis with a large number of variables generally requires a large amount of memory and computation power or a classification algorithm which overfits the training sample and generalizes poorly to new samples. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy.
Best results are achieved when an expert constructs a set of application-dependent features. Nevertheless, if no such expert knowledge is available general dimensionality reduction techniques may help. These include:
- Principal component analysis
- Semidefinite embedding
- Multifactor dimensionality reduction
- Multilinear subspace learning
- Nonlinear dimensionality reduction
- Isomap
- Kernel PCA
- Multilinear PCA
- Latent semantic analysis
- Partial least squares
- Independent component analysis
- Autoencoder
Read more about this topic: Feature Extraction
Famous quotes containing the word general:
“Anti-Nebraska, Know-Nothings, and general disgust with the powers that be, have carried this county [Hamilton County, Ohio] by between seven and eight thousand majority! How people do hate Catholics, and what a happiness it was to show it in what seemed a lawful and patriotic manner.”
—Rutherford Birchard Hayes (1822–1893)
“A general loathing of a gang or sect usually has some sound basis in instinct.”
—Ezra Pound (1885–1972)
“The general who advances without coveting fame and retreats without fearing disgrace, whose only thought is to protect his country and do good service for his sovereign, is the jewel of the kingdom.”
—Sun Tzu (6th–5th century B.C.)