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:
“Hence that general is skilful in attack whose opponent does not know what to defend; and he is skilful in defense whose opponent does not know what to attack.”
—Sun Tzu (6th–5th century B.C.)
“That sort of half sigh, which, accompanied by two or three slight nods of the head, is pity’s small change in general society.”
—Charles Dickens (1812–1870)
“Every general increase of freedom is accompanied by some degeneracy, attributable to the same causes as the freedom.”
—Charles Horton Cooley (1864–1929)