نتایج جستجو برای: non central principal component analysis
تعداد نتایج: 4632641 فیلتر نتایج به سال:
This paper extends the principal component analysis (PCA) to moderately non-stationary vector time series. We propose a method that searches for linear transformation of original series such transformed is segmented into uncorrelated subseries with lower dimensions. A columns’ rearrangement proposed regroup based on their relationships. discuss theoretical properties fixed and large dimensional...
Abstract This paper proposes an extension of principal component analysis to non-stationary multivariate time series data. A criterion for determining the number final retained components is proposed. An advance correlation matrix developed evaluate dynamic relationships among chosen components. The theoretical properties proposed method are given. Many simulation experiments show our approach ...
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Ass...
The objectives of this study were to compare nonparametric stability measures, and to identify promising high-yield and stable bread wheat (Triticum aestivum L.) genotypes in 7 environments during 2003-2005 in the central Black Sea region of Turkey. The bread wheat genotypes (20 advanced lines and 5 cultivars) were grown in a randomized complete block design with 4 replications in 7 different e...
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