Transform predictors into extracted features


z = transform(Mdl,x)



z = transform(Mdl,x) transforms the data x into the features z via the model Mdl.


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Create a feature transformation model with 100 features from the caltech101patches data.

rng('default') % For reproducibility
data = load('caltech101patches');
q = 100;
X = data.X;
Mdl = sparsefilt(X,q)
Warning: Solver LBFGS was not able to converge to a solution.
Mdl = 
            ModelParameters: [1×1 struct]
              NumPredictors: 363
         NumLearnedFeatures: 100
                         Mu: []
                      Sigma: []
                    FitInfo: [1×1 struct]
           TransformWeights: [363×100 double]
    InitialTransformWeights: []

  Properties, Methods

sparsefilt issues a warning because it stopped due to reaching the iteration limit, instead of reaching a step-size limit or a gradient-size limit. You can still use the learned features in the returned object by calling the transform function.

Transform the first five rows of the input data X to the new feature space.

y = transform(Mdl,X(1:5,:));
ans = 1×2

     5   100

Input Arguments

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Feature extraction model, specified as a SparseFiltering object or as a ReconstructionICA object. Create Mdl by using the sparsefilt function or the rica function.

Predictor data, specified as a matrix with p columns or as a table of numeric values with p columns. Here, p is the number of predictors in the model, which is Mdl.NumPredictors. Each row of the input matrix or table represents one data point to transform.

Data Types: single | double | table

Output Arguments

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Transformed data, returned as an n-by-q matrix. Here, n is the number of rows in the input data x, and q is the number of features, which is Mdl.NumLearnedFeatures.


transform converts data to predicted features by using the learned weight matrix W to map input predictors to output features.

  • For rica, input data X maps linearly to output features XW. See Reconstruction ICA Algorithm.

  • For sparsefilt, input data maps nonlinearly to output features F^(X,W). See Sparse Filtering Algorithm.


    The result of transform for sparse filtering depends on the number of data points. In particular, the result of applying transform to each row of a matrix separately differs from the result of applying transform to the entire matrix at once.

Introduced in R2017a