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:
“We raised a simple prayer
Before we left the spot,
That in the general mowing
That place might be forgot;
Or if not all so favored,
Obtain such grace of hours
That none should mow the grass there
While so confused with flowers.”
—Robert Frost (1874–1963)
“The most general deficiency in our sort of culture and education is gradually dawning on me: no one learns, no one strives towards, no one teaches—enduring loneliness.”
—Friedrich Nietzsche (1844–1900)
“It is a maxim among these lawyers, that whatever hath been done before, may legally be done again: and therefore they take special care to record all the decisions formerly made against common justice and the general reason of mankind.”
—Jonathan Swift (1667–1745)