Feature Extraction - General

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:

    “A private should preserve a respectful attitude toward his superiors, and should seldom or never proceed so far as to offer suggestions to his general in the field. If the battle is not being conducted to suit him, it is better for him to resign. By the etiquette of war, it is permitted to none below the rank of newspaper correspondent to dictate to the general in the field.”
    —Mark Twain [Samuel Langhorne Clemens] (1835–1910)

    “There seems almost a general wish of descrying the capacity and undervaluing the labour of the novelist, and of slighting the performances which have only genius, wit, and taste to recommend them.”
    —Jane Austen (1775–1817)

    “An aristocratic culture does not advertise its emotions. In its forms of expression it is sober and reserved. Its general attitude is stoic.”
    —Johan Huizinga (1872–1945)