feature extraction by reconstruction ica -凯发官方首页
feature extraction by reconstruction ica
description
reconstructionica
applies reconstruction
independent component analysis (rica) to learn a transformation that maps input
predictors to new predictors.
creation
create a reconstructionica
object by using the
rica
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 for training model
structure
this property is read-only.
parameters for training the model, returned as a structure. the structure
contains a subset of the fields that correspond to the rica
name-value pairs that were
in effect during model creation:
iterationlimit
verbositylevel
lambda
standardize
contrastfcn
gradienttolerance
steptolerance
for details, see the rica
name,value
pairs.
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
nongaussianityindicator
— non-gaussianity of sources
length-q
vector of ±1
this property is read-only.
non-gaussianity of sources, returned as a length-q
vector of ±1.
nongaussianityindicator(k) = 1
meansrica
models thek
th source as sub-gaussian.nongaussianityindicator(k) = -1
meansrica
models thek
th source as super-gaussian, with a sharp peak at 0.
data types: 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 predictors into extracted features |
examples
create reconstruction ica object
create a reconstructionica
object by using the rica
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; mdl = rica(data.x,q,'iterationlimit',100)
warning: solver lbfgs was not able to converge to a solution.
mdl = reconstructionica modelparameters: [1x1 struct] numpredictors: 363 numlearnedfeatures: 100 mu: [] sigma: [] fitinfo: [1x1 struct] transformweights: [363x100 double] initialtransformweights: [] nongaussianityindicator: [100x1 double] properties, methods
rica
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
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