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 general tendency of things throughout the world is to render mediocrity the ascendant power among mankind.”
—John Stuart Mill (1806–1873)
“I have never looked at foreign countries or gone there but with the purpose of getting to know the general human qualities that are spread all over the earth in very different forms, and then to find these qualities again in my own country and to recognize and to further them.”
—Johann Wolfgang Von Goethe (1749–1832)
“It has been the struggle between privileged men who have managed to get hold of the levers of power and the people in general with their vague and changing aspirations for equality, for justice, for some kind of gentler brotherhood and peace, which has kept that balance of forces we call our system of government in equilibrium.”
—John Dos Passos (1896–1970)