({{, (RefLink[FeatureExtractorFunction, paclet:ref/FeatureExtractorFunction])[…] 
represents a feature extractor function generated by (RefLink[FeatureExtraction, paclet:ref/FeatureExtraction]).}})

OpenersNotesSection(Spacer1)Details and Options

(RefLink[FeatureExtractorFunction, paclet:ref/FeatureExtractorFunction]) works like (RefLink[Function, paclet:ref/Function]).

(RefLink[FeatureExtractorFunction, paclet:ref/FeatureExtractorFunction])[…][data] extracts features from data.

(RefLink[FeatureExtractorFunction, paclet:ref/FeatureExtractorFunction])[…][{(Subscript[data, 1]),(Subscript[data, 2]),…}] extracts features from each of the (Subscript[data, i]).

(RefLink[FeatureExtractorFunction, paclet:ref/FeatureExtractorFunction])[…][data,prop] gives the specified property of the feature extraction associated with data.

Possible properties include:

The following options can be given:

Possible settings for (RefLink[RandomSeeding, paclet:ref/RandomSeeding]) include:

OpenersPrimaryExamplesSection(Spacer1)Examples  (3)

OpenersExampleSection0(Spacer1)Basic Examples  (1)

Train a (RefLink[FeatureExtractorFunction, paclet:ref/FeatureExtractorFunction]) on a simple dataset:

fe=FeatureExtraction[{{1.4,"A"},{1.5,"A"},{2.3,"B"},{5.4,"B"}}]

Extract features from a new example:

fe[{2.4,"A"}]

Extract features from a list of examples:

fe[{{2.4,"A"},{3.7, "B"}}]

Attempt to obtain the original data from the extracted features:

fe[{{2.4`,1.`,0.`},{3.7`,0.`,1.`}}, "OriginalData"]

OpenersExampleSection1(Spacer1)Scope  (2)

Some feature extractors can only perform an approximation of the inverse extraction:

fe=FeatureExtraction[{{1.4,1.4,5.4,5.2},{1.5,1.5,6.4,5.2},{1.2,1.2,6.2,5.2},{1.6,1.6,4.3,5.2}},"DimensionReducedVector"]

features = fe[{1.1,1.2,5.1,5.7}]

fe[features,"OriginalData"]

The (RefLink[FeatureExtraction, paclet:ref/FeatureExtraction]) property "ReconstructedData" can be used to obtain the data after extraction and reconstruction:

FeatureExtraction[{{1.4,1.4,5.4,5.2},{1.5,1.5,6.4,5.2},{1.2,1.2,6.2,5.2},{1.6,1.6,4.3,5.2}},"DimensionReducedVector","ReconstructedData"]

Some feature extractors cannot be inverted:

FeatureExtraction[{, ,,},"ImageFeatures","ReconstructedData"]

Train a feature extractor from a dataset that contains missing values:

fe=FeatureExtraction[{{1.4,Missing[],"A"},{1.5,50.2,"A"},{Missing[],42.3,"B"},{5.4,61.7,"B"}}]

The feature extractor now indicates that missing values are imputed. The feature extractor can extract features even when values are missing:

fe[{3.4,Missing[],"B"}]

({{DocumentationFooterIconsRelatedFunction.png, ({{See Also}, {(RefLink[FeatureExtraction, paclet:ref/FeatureExtraction])  ▪  (RefLink[FeatureExtractor, paclet:ref/FeatureExtractor])  ▪  (RefLink[DimensionReducerFunction, paclet:ref/DimensionReducerFunction])  ▪  (RefLink[ClassifierFunction, paclet:ref/ClassifierFunction])  ▪  (RefLink[NearestFunction, paclet:ref/NearestFunction])}})}})

({{DocumentationFooterIconsRelatedGuide.png, ({{Related Guides}, {▪(RefLinkPlain[Machine Learning, paclet:guide/MachineLearning])}})}})