The Schatten p-quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction, etc. Meanwhile, the l_(2,1)-regularized matrix optimization m...The Schatten p-quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction, etc. Meanwhile, the l_(2,1)-regularized matrix optimization models are also popularly used for its joint sparsity. Naturally, the pseudo matrix norm l_(2,p) is expected to carry over the advantages of both l_p and l_(2,1). This paper proposes a mixed l_(2,q)-l_(2,p) matrix minimization approach for multi-image face recognition. To uniformly solve this optimization problem for any q ∈ [1,2] and p ∈(0,2], an iterative quadratic method(IQM) is developed. IQM is proved to iescend strictly until it gets a stationary point of the mixed l_(2,q)-l_(2,p)matrix minimization. Moreover, a more practical IQM is presented for large-scale case. Experimental results on three public facial image databases show that the joint matrix minimization approach with practical IQM not only saves much computational cost but also achievez better performance in face recognition than state-of-the-art methods.展开更多
Due to the drawbacks in Support Vector Machine（SVM）parameter optimization,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA）was proposed,and the learning ability in practical speech emotion recognition was impro...Due to the drawbacks in Support Vector Machine（SVM）parameter optimization,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA）was proposed,and the learning ability in practical speech emotion recognition was improved.Firstly,we introduced Simulated Annealing（SA）,Immune Vaccination（Iv）,Gaussian mutation and chaotic disturbance into the basic SFLA,which bManced the search efficiency and population diversity effectively.Secondly,Im-SFLA Was applied to the optimization of SVM parameters,and an Im-SFLA-SVM method Was proposed.Thirdly,the acoustic features of practical speech emotion,such aS ridgetiness,were analyzed.The pitch frequency,short-term energy,formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories,and we constructed a 144-dimensional emotion feature vector for recognition and reduced to 4-dimension by adopting Linear Discriminant Analysis（LDA） Finally,the Im-SFLA-SVM method Was tested on the practical speech emotion database,and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM（SFLA-SVM）method,Particle Swarm Optimization algorithm optimization-SVM（PSo-SVM） method,basic SVM,Gaussian Mixture Model（GMM）method and Back Propagation（BP）neural network method.The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.11471159 and 61661136001)
文摘The Schatten p-quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction, etc. Meanwhile, the l_(2,1)-regularized matrix optimization models are also popularly used for its joint sparsity. Naturally, the pseudo matrix norm l_(2,p) is expected to carry over the advantages of both l_p and l_(2,1). This paper proposes a mixed l_(2,q)-l_(2,p) matrix minimization approach for multi-image face recognition. To uniformly solve this optimization problem for any q ∈ [1,2] and p ∈(0,2], an iterative quadratic method(IQM) is developed. IQM is proved to iescend strictly until it gets a stationary point of the mixed l_(2,q)-l_(2,p)matrix minimization. Moreover, a more practical IQM is presented for large-scale case. Experimental results on three public facial image databases show that the joint matrix minimization approach with practical IQM not only saves much computational cost but also achievez better performance in face recognition than state-of-the-art methods.
基金supported by the National Nature Science Foundation(61231002,61273266,51075068)the Doctoral Fund of Ministry of Education of China(20110092130004)+1 种基金the Postdoctoral Fund of Ministry of Education of China(2012M520973)the Open Research Foundation of Key Laboratory(B) of Underwater Acoustic Signal Processing of Ministry of Education of Southeast University under Grant(UASP1202)
文摘Due to the drawbacks in Support Vector Machine（SVM）parameter optimization,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA）was proposed,and the learning ability in practical speech emotion recognition was improved.Firstly,we introduced Simulated Annealing（SA）,Immune Vaccination（Iv）,Gaussian mutation and chaotic disturbance into the basic SFLA,which bManced the search efficiency and population diversity effectively.Secondly,Im-SFLA Was applied to the optimization of SVM parameters,and an Im-SFLA-SVM method Was proposed.Thirdly,the acoustic features of practical speech emotion,such aS ridgetiness,were analyzed.The pitch frequency,short-term energy,formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories,and we constructed a 144-dimensional emotion feature vector for recognition and reduced to 4-dimension by adopting Linear Discriminant Analysis（LDA） Finally,the Im-SFLA-SVM method Was tested on the practical speech emotion database,and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM（SFLA-SVM）method,Particle Swarm Optimization algorithm optimization-SVM（PSo-SVM） method,basic SVM,Gaussian Mixture Model（GMM）method and Back Propagation（BP）neural network method.The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.
