Classical dimension reduction algorithm -- princip
2016-08-23
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PCA algorithm is mainly used to reduce the dimension, the data from high dimension to low dimension, simplify the expression of data. The specific algorithm steps are as follows:1, the sample matrix of the center of the sample matrix (matrix X each line is a sample)2, seeking covariance matrix3, the characteristic value, characteristic vector4, according to the contribution rate, to determine the number of feature vectors to form a transformation matrix5, take the former J column vector constitute the transformation matrix6, the sample matrix is projected onto the transform matrix, and the reduced dimension matrix is obtained.python
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