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Classical dimension reduction algorithm PCA
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Application background Matlab to achieve the classic dimension reduction algorithm - the principal component analysis (PCA) algorithm, mainly used for data reduction, to retain the other side of the data set difference to contribute to the maximum number of features to achieve the purpose of simplifying the data set.Steps to reduce the dimension of data:1, the original data in each of the samples with a vector representation, the combination of all samples constitute a matrix, usually need to process the sample matrix to get a neutral sample matrix2, seeking the covariance matrix of the sample matrix3, find the eigenvalues and eigenvectors of the covariance matrix4, the obtained feature vectors are combined to form a mapping matrix according to the size of the eigenvalues. And according to the specified PCA to retain the characteristics of the number of the map out of the mapping matrix before the n row or the n column as the final mapping matrix.5, using the mappin
qq:骆驼
2016-08-23
0
1
Classical dimension reduction algorithm -- princip
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Application background 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.
qq:骆驼
2016-08-23
0
1
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