Discriminative canonical correlation analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists parts: (i) mean-centering that subtracts the sample mean from and (ii) solving generalized eigenvalue problem. The cost expensive when dealing with large number high-dimensional samples. To...

Cornelian cherry is one of the most important small fruits in Arasbaran region, with wide applications in medicines and food products. In this study, the relationship among 28 quantitative and qualitative traits related to fruit, leaf, tree, and flower of 20 cornelian cherry genotypes was evaluated. Significant positive as well as negative correlations were found among some important quantitati...

Knowledge of genetic diversity and relationships between genotypes is mainly important for selection of parental genotypes. Moreover, assessing diversity across and within crop varieties is important to improve the description of collections in gene banks and in on-farm conservation practices. In order to evaluation and study of genetic diversity in the bean in normal condition, forty-five bean...

In this study, a two-step principal component analysis (TS-PCA) is proposed to handle the dynamic characteristics of chemical industrial processes in both steady state and unsteady state. Differently from the traditional dynamic PCA (DPCA) dealing with the static cross-correlation structure and dynamic auto-correlation structure in process data simultaneously, TS-PCA handles them in two steps: ...

A hypercomplex representation of DNA is proposed to facilitate comparing DNA sequences with fuzzy composition. With the hypercomplex number representation, the conventional sequence analysis method, such as, dot matrix analysis, dynamic programming, and cross-correlation method have been extended and improved to align DNA sequences with fuzzy composition. The hypercomplex dot matrix analysis ca...