FeatureExtraction

FeatureExtraction[{example₁,example₂,…}]

generates a FeatureExtractorFunction[…] trained from the examples given.

FeatureExtraction[examples,extractor]

uses the specified feature extractor method.

FeatureExtraction[examples,{extractor₁,extractor₂,…}]

applies the extractor_i in sequence to generate a feature extractor.

FeatureExtraction[examples,specext]

uses the extractor methods specified by ext on parts of examples specified by spec.

FeatureExtraction[examples,{spec₁ext₁,spec₂ext₂,…}]

uses the extractor methods ext_i on parts of examples specified by the spec_i.

FeatureExtraction[examples,extractor,props]

gives the feature extraction properties specified by props.

Details and Options

FeatureExtraction can be used on many types of data, including numerical, textual, sounds, and images, as well as combinations of these.
Each example_i can be a single data element, a list of data elements, an association of data elements, or a Dataset object.
FeatureExtraction[examples] returns a FeatureExtractorFunction[…] that can be applied to specific data.
Possible feature extractor methods to use in FeatureExtraction include:

	Automatic	automatic extraction
	"AudioFeatures"	sequence of semantic vectors from an audio object
	"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
	"LPC"	audio linear prediction coefficients
	"LowerCasedText"	text with each character lowercase
	"MelSpectrogram"	audio spectrogram with logarithmic frequencies bins
	"MFCC"	audio mel-frequency cepstral coefficients vectors sequence
	"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
	"Spectrogram"	audio spectrogram
	"TFIDF"	term frequency-inverse document frequency vector
	"WordVectors"	semantic vectors sequence from a text (English only)
	Identity	give data unchanged
	f	applies function f to each example
	{extractor₁,extractor₂,…}	use a sequence of extractors in turn

Feature extractor methods are applied to data elements with whose types they are compatible. Other data elements are returned unchanged.
FeatureExtraction[examples] is equivalent to FeatureExtraction[examples,Automatic], which is typically equivalent to FeatureExtraction[examples,"NumericVector"].
The "NumericVector" method will typically convert examples to numeric vectors, impute missing data, and reduce the dimension using DimensionReduction.
In FeatureExtraction[examples,extractors,props], props can be a single property or a list of properties. Possible properties include:

	"ExtractorFunction"	FeatureExtractorFunction[…] (default)
	"ExtractedFeatures"	examples after feature extraction
	"ReconstructedData"	examples after extraction and inverse extraction
	"FeatureDistance"	FeatureDistance[…] generated from the extractor

In FeatureExtraction[examples,specext] or FeatureExtraction[examples,{spec₁ext₁,…}], possible forms for spec and the spec_i include:

	All	all parts of each example
	i	i 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 "name_i" in each example

Parts not mentioned in spec or the spec_i are dropped for the purpose of extracting features.
In FeatureExtraction[examples,{spec₁ext₁,…}], the ext_i are all applied separately to examples.
The following options can be given:

FeatureNames	Automatic	names to assign to elements of the example_i
FeatureTypes	Automatic	feature types to assume for elements of the example_i
RandomSeeding	1234	what seeding of pseudorandom generators should be done internally

	Automatic	automatically reseed every time the function is called
	Inherited	use externally seeded random numbers
	seed	use an explicit integer or strings as a seed

FeatureExtraction[…,"ExtractedFeatures"] is equivalent to FeatureExtract[…].
FeatureExtraction[…,"FeatureDistance"] is equivalent to FeatureDistance[FeatureExtraction[…]].