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:
“The man who would change the name of Arkansas is the original, iron-jawed, brass-mouthed, copper-bellied corpse-maker from the wilds of the Ozarks! He is the man they call Sudden Death and General Desolation! Sired by a hurricane, dam’d by an earthquake, half-brother to the cholera, nearly related to the smallpox on his mother’s side!”
—Administration in the State of Arka, U.S. public relief program (1935-1943)
“He who never sacrificed a present to a future good or a personal to a general one can speak of happiness only as the blind do of colors.”
—Olympia Brown (1835–1900)
“Pleasure is necessarily reciprocal; no one feels it who does not at the same time give it. To be pleased, one must please. What pleases you in others, will in general please them in you.”
—Philip Dormer Stanhope, 4th Earl Chesterfield (1694–1773)