Wolfram Language & System 11.0 (2016)|Legacy Documentation

This is documentation for an earlier version of the Wolfram Language.View current documentation (Version 11.2)

FeatureExtractor

FeatureExtractor
is an option for functions such as Classify that specifies how features should be extracted.

DetailsDetails

  • Possible settings for FeatureExtractor include:
  • FeatureExtractorFunction[]apply the given extractor function
    extractorapply the specified feature extractor method
    {extractor1,extractor2,}apply the sequence of extractor methods in turn
    specextapply extractor ext to data parts specified by spec
    {spec1ext1,spec2ext2,}apply extractors exti to data parts specified by the speci
  • Possible feature extractor methods to use in FeatureExtractor include:
  • Automaticautomatic extraction
    "ConformedData"conformed images, colors, dates, etc.
    "DiscretizedVector"discretized numerical data
    "DimensionReducedVector"reduced-dimension numeric vectors
    "FaceFeatures"semantic vector from an image of a human face
    "ImageFeatures"semantic vector from an image
    "IndicatorVector"nominal data "one-hot encoded" with indicator vectors
    "IntegerVector"nominal data encoded with integers
    "MissingImputed"data with missing values imputed
    "NumericVector"numeric vector from any data
    "PixelVector"vector of pixel values from an image
    "StandardizedVector"numeric data processed with Standardize
    "SegmentedCharacters"text segmented into characters
    "SegmentedWords"text segmented into words
    "TFIDF"term frequency-inverse document frequency vector
    Identitygive data unchanged
    {extractor1,extractor2,}use a sequence of extractors in turn
  • By default, FeatureExtractor->Identity.
  • When the feature extractor method is not a FeatureExtractorFunction[], the feature extraction will be learned from the data.
  • With the settings specext or {spec1ext1,}, possible forms for spec and the speci include:
  • Allall parts of each example
    ii^(th) part of each example
    {i1,i2,}parts i1, i2, of each example
    "name"part with the specified name in each example
    {"name1","name2",}parts with names "namei" in each example
  • Parts not mentioned in spec or the speci are dropped for the purpose of extracting features.

ExamplesExamplesopen allclose all

Basic Examples  (2)Basic Examples  (2)

Train a FeatureExtractorFunction on a simple dataset:

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Use the feature extractor function as a preprocessing step in Classify:

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Train a classifier using the extractor method "ImageFeatures" as a preprocessing step:

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Classify a new image:

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Introduced in 2016
(11.0)