هل يوجد built in function in matlabلتى هي priciple component analysisab ل pca وا

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الاقتران موجود في ال Statistics Toolbox

وهو :
COEFF = pcacov(V)
[COEFF,latent] = pcacov(V)
[COEFF,latent,explained] = pcacov(V)

we sum the absolute factor loadings weighted by the corresponding percentage of explained overall variance. The result is normalized by the sum of variances of all PCs. We call this measure [FONT=Times New Roman,Times New Roman][SIZE=1][FONT=Times New Roman,Times New Roman][SIZE=1]Weighted Average LoaDing Indicator [/size][/font][/size][/font]SIZE=1. It is computed as defined in Equation 1. [/size]
المعادلة في الصورة

[LEFT]Where [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]fj [/font][/font]is the [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]j[/font][/font]-th feature from a set of[FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]F[/font][/font]features and the [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]ci [/font][/font]are the [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]C [/font][/font]principal components. The variance of the [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]i[/font][/font]-th PC is denoted by σ2([FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]ci[/font][/font]). The factor loading matrix [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]L [/font][/font]is a [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]R[/font][/font]CxF matrix with [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]C [/font][/font]columns and [FONT=Times New Roman,Times New Roman][FONT=Times New Roman,Times New Roman]F [/font][/font]rows. [/left]
The WALDI of a feature is a measure of its information content in terms of orthogonal variances in the data. This value is proportional to the expressiveness of a feature. However, it does not contain information about redundancies among different features. This information can be derived from the factor loading matrix.
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