MATLAB File Help: prtPreProcLda
  prtPreProcLda  Linear discriminant analysis processing
    preProc = prtPreProcLda creates a linear discriminant pre
    processing object. A prtPreProcLda object projects the input data
    onto a linear space that best separates class labels
    A prtPreProcLda object has the following properties:
    nComponents - The number of dimensions to project the data onto.
                  This must less than or equal to the input data's
                  number of features, and less than or equal to the 
                  input data sets number of classes.
    A prtPreProcLda object also inherits all properties and functions from
    the prtAction class
    More information about LDA can be found at the following URL:
    dataSet = prtDataGenIris;               % Load a dataset
    dataSet = dataSet.retainFeatures(1:3);  % Retain the first 3 features
    lda = prtPreProcLda;                    % Create the pre-processor
    lda = lda.train(dataSet);               % Train
    dataSetNew =;          % Run
    % Plot the results
    subplot(2,1,1); plot(dataSet);
    title('Original Data');
    subplot(2,1,2); plot(dataSetNew);
    title('LDA Projected Data');
See Also
Class Details
Superclasses prtPreProcClass
Sealed false
Construct on load false
Constructor Summary
prtPreProcLda Linear discriminant analysis processing 
Property Summary
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
globalMean The global mean 
isCrossValidateValid True 
isSupervised False 
isTrained Indicates if prtAction object has been trained. 
nComponents The number of LDA components 
name Linear discriminant analysis 
nameAbbreviation LDA 
projectionMatrix The projection matrix 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
Method Summary
  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.