Abstract：Since the exsiting dictionary learning methods can not effectively construct discriminant structured dictionary, a discriminant dictionary learning method with discriminant and representative ability was proposed and applied in software defect detection. Firstly, sparse representation model was redesigned to train structured dictionary by adding the discriminant constraint term into the object function, which made the class-dictionary have strong representation ability for the corresponding class-samples but poor representation ability for the irrelevant class-samples. Secondly, the Fisher criterion discriminant term was added to make the representative coefficients have discriminant ability in different classes. Finally, the optimization of the designed dictionary learning model was solved to obtain strongly structured and sparsely representative dictionary. The NASA defect dataset was selected as the experiment data, and compared with Principal Component Analysis (PCA), Logistics Regression (LR), decision tree, Support Vector Machine (SVM) and the typical dictionary learning method, the accuracy and F-measure value of the proposed method were both increased. Experimental results indicate that the proposed method can increase detection accuracy with improving the classifier performance.
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2016, Vol. 36 
