Feature extraction by sparse filtering
SparseFiltering uses sparse filtering to
learn a transformation that maps input predictors to new predictors.
Create a SparseFiltering object using the sparsefilt function.
FitInfo — Fitting historyThis property is read-only.
Fitting history, returned as a structure with two fields:
Iteration — Iteration numbers from 0 through the
final iteration.
Objective — Objective function value at each
corresponding iteration. Iteration 0 corresponds to the initial values, before
any fitting.
Data Types: struct
InitialTransformWeights — Initial feature transformation weightsp-by-q matrixThis property is read-only.
Initial feature transformation weights, returned as a
p-by-q matrix, where p is the number of predictors passed in X and
q is the number of features that you want. These weights are the
initial weights passed to the creation function. The data type is single when the
training data X is single.
Data Types: single | double
ModelParameters — Parameters used for training modelThis property is read-only.
Parameters used for training the model, returned as a structure. The
structure contains a subset of the fields that corresponds to the sparsefilt name-value pairs
that were in effect during model creation:
IterationLimit
VerbosityLevel
Lambda
Standardize
GradientTolerance
StepTolerance
For details, see the sparsefilt name-value pairs in
the documentation.
Data Types: struct
Mu — Predictor means when standardizingp-by-1 vectorThis property is read-only.
Predictor means when standardizing, returned as a
p-by-1 vector. This property is nonempty when
the Standardize name-value pair is
true at model creation. The value is the vector of predictor
means in the training data. The data type is single when the training data
X is single.
Data Types: single | double
NumLearnedFeatures — Number of output featuresThis property is read-only.
Number of output features, returned as a positive integer. This value is
the q argument passed to
the creation function, which is the requested number of features to
learn.
Data Types: double
NumPredictors — Number of input predictorsThis property is read-only.
Number of input predictors, returned as a positive integer. This value is
the number of predictors passed in X to the creation
function.
Data Types: double
Sigma — Predictor standard deviations when standardizingp-by-1 vectorThis property is read-only.
Predictor standard deviations when standardizing, returned as a
p-by-1 vector. This property is nonempty when
the Standardize name-value pair is
true at model creation. The value is the vector of predictor
standard deviations in the training data. The data type is single when the training data
X is single.
Data Types: single | double
TransformWeights — Feature transformation weightsp-by-q matrixThis property is read-only.
Feature transformation weights, returned as a
p-by-q matrix, where p is the number of predictors passed in X and
q is the number of features that you want. The data type is
single when the training data X is single.
Data Types: single | double
transform | Transform predictors into extracted features |
Create a SparseFiltering object by using the sparsefilt function.
Load the SampleImagePatches image patches.
data = load('SampleImagePatches');
size(data.X)ans = 1×2
5000 363
There are 5,000 image patches, each containing 363 features.
Extract 100 features from the data.
rng default % For reproducibility Q = 100; obj = sparsefilt(data.X,Q,'IterationLimit',100)
Warning: Solver LBFGS was not able to converge to a solution.
obj =
SparseFiltering
ModelParameters: [1x1 struct]
NumPredictors: 363
NumLearnedFeatures: 100
Mu: []
Sigma: []
FitInfo: [1x1 struct]
TransformWeights: [363x100 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.
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