A scalar structure holding the options the fit ran with: IterationLimit, Lambda, Standardize, InitialTransformWeights, GradientTolerance, StepTolerance and Solver. This property is read-only.

A positive integer P, the number of columns of the training data. This property is read-only.

A positive integer Q, the number of features the learned transformation produces. This property is read-only.

A column vector with one entry per predictor, the mean of each column of the training data. It is empty unless 'Standardize' was true. This property is read-only.

A column vector with one entry per predictor, the standard deviation of each column of the training data. It is empty unless 'Standardize' was true. This property is read-only.

A scalar structure with the fields Iteration and Objective, 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. This property is read-only.

The trajectory is this implementation’s own. The default 'quasinewton' solver minimises through Octave’s fminunc, and '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.

A P-by-Q matrix of learned weights. The transform method applies it to data. This property is read-only.

A P-by-Q matrix, the weights the fit started from. It is the matrix given as 'InitialTransformWeights' when one was given, and the random start the fit drew otherwise. This property is read-only.

statistics: Mdl = SparseFiltering (X, Q, …)

statistics: Z = transform (Mdl, X)