Leave one out cross validation-HELP
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wayne asera on 14 Dec 2017
I am implementing a fingerprint liveness detection algorithm by extracting the features using a modified combination of Weber local descriptor and Centralized Binary pattern, a 9x32 vector matrix is generated. So after extracting the features from the training dataset of 2011 Liveness Detection Competition the resulting vector is 18000x32. So I want to use the Leave one out cross validation to determine its accuracy. Could you please give an example? So far I have seen examples of KFold cross validation in the documentation but none for leaveout. At the moment I used a (10)KFold cross validation to train the resulting vector then extracted the code and did some minor changes such as: partitionedModel = crossval(trainedClassifier.ClassificationEnsemble, 'Kfold', 10)=>partitionedModel = crossval(trainedClassifier.ClassificationEnsemble, 'Leaveout', 'on'); Could you please point me to the right direction?
Thank you very much
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