Wolfram Language & System Documentation Center

FeatureExtractor

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

Details
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
See Also
Related Guides
History
Cite this Page

FeatureExtractor

FeatureExtractor

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

Details

  • 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 include:
  • Automaticautomatic extraction
    Identitygive data unchanged
    "ConformedData"conformed images, colors, dates, etc.
    "NumericVector"numeric vector from any data
    fapplies function f to each example
    {extractor1,extractor2,…}use a sequence of extractors in turn
  • Additional feature extractor methods can also be used for each data type.
  • Numeric data:
  • "DiscretizedVector"discretized numerical data
    "DimensionReducedVector"reduced-dimension numeric vectors
    "MissingImputed"data with missing values imputed
    "StandardizedVector"numeric data processed with Standardize
  • Nominal data:
  • "IndicatorVector"nominal data "one-hot encoded" with indicator vectors
    "IntegerVector"nominal data encoded with integers
  • Text:
  • "LowerCasedText"text with each character lowercase
    "SegmentedCharacters"text segmented into characters
    "SegmentedWords"text segmented into words
    "SentenceVector"semantic embedding vector from a text
    "TFIDF"term frequency-inverse document frequency vector
    "WordVectors"semantic vectors sequence from a text (English only)
  • Images:
  • "FaceFeatures"semantic vector from an image of a human face
    "ImageFeatures"semantic vector from an image
    "PixelVector"vector of pixel values from an image
  • Audio objects:
  • "AudioFeatures"sequence of semantic vectors from an audio object
    "AudioFeatureVector"semantic vector from an audio object
    "LPC"audio linear prediction coefficients
    "MelSpectrogram"audio spectrogram with logarithmic frequency bins
    "MFCC"audio mel-frequency cepstral coefficient vectors sequence
    "SpeakerFeatures"sequence of semantic speaker vectors
    "SpeakerFeatureVector"semantic vector for a speaker
    "Spectrogram"audio spectrogram
  • Video objects:
  • "VideoFeatures"sequence of semantic vectors from a video object
    "VideoFeatureVector"semantic vector from a video object
  • Graphs:
  • "GraphFeatures"numeric vector summarizing graph properties
  • Molecules:
  • "AtomPairs"Boolean vector from pairs of atoms and the path lengths between them
    "MoleculeExtendedConnectivity"Boolean vector from enumerated molecule subgraphs
    "MoleculeFeatures"numeric vector summarizing molecule properties
    "MoleculeTopologicalFeatures"Boolean vector from circular atom neighborhoods
  • By default, FeatureExtractorIdentity.
  • Typically, the value of FeatureExtractor is interpreted as a preprocessing step: it will not replace the other feature extractors used by the function.
  • When the feature extractor method is not a FeatureExtractorFunction[…] or a custom function, 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.
  • In functions such as Classify, Predict, DimensionReduction or ClusterClassify, FeatureExtractor"Minimal" indicates that the internal preprocessing should be as simple as possible.

Examples

open all close all

Basic Examples  (3)

Train a FeatureExtractorFunction on a simple dataset:

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

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

Classify a new image:

Generate a predictor function using FeatureExtractor to preprocess the data using a custom function:

Add the "StandardizedVector" method to the preprocessing pipeline:

Use the predictor on new data:

Scope  (1)

Train a classifier on texts preprocessed by custom functions and an extractor method:

Wolfram Research (2016), FeatureExtractor, Wolfram Language function, https://reference.wolfram.com/language/ref/FeatureExtractor.html (updated 2025).

Text

Wolfram Research (2016), FeatureExtractor, Wolfram Language function, https://reference.wolfram.com/language/ref/FeatureExtractor.html (updated 2025).

CMS

Wolfram Language. 2016. "FeatureExtractor." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2025. https://reference.wolfram.com/language/ref/FeatureExtractor.html.

APA

Wolfram Language. (2016). FeatureExtractor. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/FeatureExtractor.html

BibTeX

@misc{reference.wolfram_2025_featureextractor, author="Wolfram Research", title="{FeatureExtractor}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/FeatureExtractor.html}", note=[Accessed: 01-December-2025]}

BibLaTeX

@online{reference.wolfram_2025_featureextractor, organization={Wolfram Research}, title={FeatureExtractor}, year={2025}, url={https://reference.wolfram.com/language/ref/FeatureExtractor.html}, note=[Accessed: 01-December-2025]}