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Kernel-based discriminant analysis is an effective nonlinear mechanism for pattern analysis. Conventional kernel-based discriminant analysis mainly based on a single kernel function may be insufficient when dealing with datasets with complicated geometric structures. A combination of multiple kernels is able to represent the complementary information of the original data from multiple views and thereby improves recognition performance. However, the discriminant analysis methods based on the combination of multiple kernels face the challenges of optimizing the weights of the ''base kernels'' and the heavy computational burden. To address these challenges, this paper proposes a novel multi-kernel discriminant analysis method based on support vectors (MKDASV) to represent the data structure more effectively by incorporating the between-class and within-class information. First, the multi-kernel SVM algorithm is utilized to obtain the weight of each ''base kernel'' and the support vectors; and then the criteria of discriminant analysis method are constructed by taking into account both the margin maximizing classification theory of SVM and the expression of the within-class scatter in LDA algorithm; and finally, to effectively reduce the amount of computation, only the support vectors are used as the training samples to participate in the dimensionality reduction operation. The experimental results on six standard databases validated that our proposed method outperformed the other five methods in terms of classification accuracy and the computational efficiency as well. INDEX TERMS Kernel discriminant analysis, multi-kernel SVM, support vectors.

This paper presents a pork quality evaluation method based on the hyperspectral image datasets of 96 pork samples in the range of 400–1000[Formula: see text]nm. First, through the K-medoids clustering algorithm based on manifold distance, 30 important wavelengths are selected from 753 wavelengths, and final 8 optimum wavelengths are obtained based on the discriminant value and the spectral resolution. Then, the two-dimensional Gabor wavelet transform is used to extract the eight texture features of the image under the final eight wavelengths respectively, to form a 64-dimensional features of pork quality. Finally, using the fussy C-means (FCM) algorithm based on Isomap dimension reduction, the pork quality evaluation model is constructed. The result of wavelength extraction experiments show that although there is a strong linear correlation between adjacent bands in the hyperspectral image, there is an obvious nonlinear manifold relation in the whole band. Therefore, the K-medoids clustering algorithm based on manifold distance in this paper is more reasonable than the traditional principal component analysis (PCA) in characteristic wavelength selection. According to the experiment of pork quality evaluation, two-dimensional Gabor wavelet transform can extract the texture characteristics of pork better. Compared with the FCM algorithm based on PCA, the FCM algorithm based on Isomap can better solve the high-dimensional clustering problem, and can distinguish fresh chilled meat, frozen-thawed meat and spoiled meat accurately. The study shows that hyperspectral image technology can be used in pork quality evaluation.

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