A SparseFiltering object stores the transformation learned by sparsefilt for extracting features from data. Create one with sparsefilt; apply it to data with the transform method.

An object of this class has the following properties:

• TransformWeights: the $P\; \times \; Q$ matrix of learned transformation weights.
• Mu, Sigma: the per-predictor mean and standard deviation used when 'Standardize' is true (empty otherwise), each a column vector with one entry per predictor.
• FitInfo: a structure with the Iteration indices of the fit and the Objective value at each of them, both column vectors of the same length. Iteration counts from zero and Objective(1) is the objective at the starting weights, so the last entry of each is the solution the fit returned.

  The values along that trajectory are this implementation’s own. Under the default 'quasinewton' solver the minimisation runs through Octave’s fminunc; 'Solver', 'lbfgs' selects the limited-memory BFGS solver MATLAB uses. Either way the steps taken from the same starting weights differ from MATLAB’s, so the length of the history and the iteration counts differ, and on an objective this far from convex the optimum reached need not be MATLAB’s either.

• ModelParameters: a structure of the options used for the fit.
• NumPredictors, NumLearnedFeatures: the input dimension $P$ and the number of learned features $Q$.
• InitialTransformWeights: the starting weights used by the fit.

See also: sparsefilt, rica

Source Code: SparseFiltering