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:
“Even more important than the discovery of Columbus, which we are gathered together to celebrate, is the fact that the general government has just discovered women.”
—Bertha Honore Potter Palmer (1849–1918)
“Women born at the turn of the century have been conditioned not to speak openly of their wedding nights. Of other nights in bed with other men they speak not at all. Today a woman having bedded with a great general feels free to tell us that in bed the general could not present arms. Women of my generation would have spared the great general the revelation of this failure.”
—Jessamyn West (1907–1984)
“Must I remind you that a chain is no stronger than its weakest link?”
—Jerome Cady, U.S. screenwriter, and Lewis Milestone. General Mitsubi (Richard Loo)