MATLAB File Help: prtPreProcPca |
prtPreProcPca Principle Component Analysis PCA = prtPreProcPca creates a Principle Component Analysis object. PCA = prtPreProcPca('nComponents',N) constructs a prtPreProcPCP object PCA with nComponents set to the value N. A prtPreProcPca object has the following properites: nComponents - The number of principle componenets A prtPreProcPca object also inherits all properties and functions from the prtAction class Example: dataSet = prtDataGenFeatureSelection; % Load a data set pca = prtPreProcPca; % Create a prtPreProcPca object pca = pca.train(dataSet); % Train the prtPreProcPca object dataSetNew = pca.run(dataSet); % Run % Plot plot(dataSetNew); title('PCA Projected Data');
Superclasses | prtPreProc |
Sealed | false |
Construct on load | false |
prtPreProcPca | Principle Component Analysis |
dataSet | The training prtDataSet, only stored if verboseStorage is true. |
dataSetSummary | Structure that summarizes prtDataSet. |
isCrossValidateValid | True |
isSupervised | False |
isTrained | Indicates if prtAction object has been trained. |
means | A vector of the means |
nComponents | The number of principle components |
name | Principal Component Analysis |
nameAbbreviation | PCA |
pcaVectors | The PCA vectors. |
showProgressBar | |
totalPercentVarianceCumulative | reduced dimension data as a |
totalVariance | training data |
totalVarianceCumulative | dimension data. |
trainingTotalVariance | The total variance contained in the |
userData | User specified data |
verboseStorage | Specifies whether or not to store the training prtDataset. |
crossValidate | Cross validate prtAction using prtDataSet and cross validation keys. | |
get | get the object properties | |
kfolds | Perform K-folds cross-validation of prtAction | |
optimize | Optimize action parameter by exhaustive function maximization. | |
run | Run a prtAction object on a prtDataSet object. | |
set | set the object properties | |
train | Train a prtAction object using training a prtDataSet object. |