SparseFiltering

Feature extraction by sparse filtering

Description

SparseFiltering uses sparse filtering to learn a transformation that maps input predictors to new predictors.

Creation

Create a SparseFiltering object using the sparsefilt function.

Properties

FitInfo — Fitting history

structure

This 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 weights

p-by-q matrix

This 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 model

structure

This 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 standardizing

p-by-1 vector

This 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 features

positive integer

This 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 predictors

positive integer

This 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 standardizing

p-by-1 vector

This 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 weights

p-by-q matrix

This 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

Object Functions

transform

Transform predictors into extracted features

Examples

Create Sparse Filter

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: []

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.

Version History

Introduced in R2017a