FEATURE TRANSFORMATION USING KERNEL MINIMUM CLASSIFICATION ERROR FOR PATTERN AND SPEECH RECOGNITION

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ZAMANI DEHKORDI BEHZAD; AKBARI AHMAD; NASERSHARIF BABAK

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Journal: SIGNAL AND DATA PROCESSING Year:2010 | Volume:- | Issue:1 (SERIAL 13) Page(s): 3-18

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Title

FEATURE TRANSFORMATION USING KERNEL MINIMUM CLASSIFICATION ERROR FOR PATTERN AND SPEECH RECOGNITION

Author(s)

ZAMANI DEHKORDI BEHZAD | AKBARI AHMAD | NASERSHARIF BABAK | Issue Writer Certificate

Keywords

FEATURE TRANSFORMATIONQ2

LINEAR DISCRIMINANT ANALYSIS (LDA)Q3

PRINCIPAL COMPONENT ANALYSIS (PCA)Q3

MINIMUM CLASSIFICATION ERROR (MCE)Q3

KERNEL FUNCTIONQ3

Abstract

FEATURE TRANSFORMATION methods can be divided into two linear and nonlinear approaches. The main idea of kernel method is that if the input feature space is transformed nonlinearly to a high-dimensional space, then transformed space will become linearly separable. This separation can be obtained according to different criteria. In kernel linear discriminative analysis (KLDA), the criterion is more discrimination between features in the new space. On the contrary, the kernel principal component analysis (KPCA) is based on more feature orthogonalization in the mapped space. In this paper, as criterion, we propose to minimize the classification error in the space created by the kernel. We presented and formulated our method as the name of KMCE (kernel minimum classification error). Our experiments are performed on UCI data sets using the different classification methods and compared with conventional linear and kernel based FEATURE TRANSFORMATION techniques. Results show that our method has a higher recognition rate than other mentioned methods in the case of distance based classifiers. In addition, the performance of KMCE is as well as other methods for statistical and decision tree based classification approaches. Also, we conducted some speech recognition experiments on Aurora2. Results indicate that KMCE outperforms other nonlinear FEATURE TRANSFORMATION methods.

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APA: Copy

ZAMANI DEHKORDI, BEHZAD, AKBARI, AHMAD, & NASERSHARIF, BABAK. (2010). FEATURE TRANSFORMATION USING KERNEL MINIMUM CLASSIFICATION ERROR FOR PATTERN AND SPEECH RECOGNITION. SIGNAL AND DATA PROCESSING, -(1 (SERIAL 13)), 3-18. SID. https://sid.ir/paper/160697/en

Vancouver: Copy

ZAMANI DEHKORDI BEHZAD, AKBARI AHMAD, NASERSHARIF BABAK. FEATURE TRANSFORMATION USING KERNEL MINIMUM CLASSIFICATION ERROR FOR PATTERN AND SPEECH RECOGNITION. SIGNAL AND DATA PROCESSING[Internet]. 2010;-(1 (SERIAL 13)):3-18. Available from: https://sid.ir/paper/160697/en

IEEE: Copy

BEHZAD ZAMANI DEHKORDI, AHMAD AKBARI, and BABAK NASERSHARIF, “FEATURE TRANSFORMATION USING KERNEL MINIMUM CLASSIFICATION ERROR FOR PATTERN AND SPEECH RECOGNITION,” SIGNAL AND DATA PROCESSING, vol. -, no. 1 (SERIAL 13), pp. 3–18, 2010, [Online]. Available: https://sid.ir/paper/160697/en

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