Image dimension reduction clustering method based on fuzzy theory
A clustering method and fuzzy theory technology, applied in the field of machine learning, can solve the problems of losing category information, reducing clustering accuracy, reducing algorithm efficiency, etc., to achieve the goal of speeding up computing time, improving algorithm efficiency, and reducing computational complexity Effect
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[0103] like figure 1 As shown, the fuzzy principal component dimensionality reduction clustering method includes the following steps:
[0104] Take the Control dataset as an example to introduce the method flow. The Control dataset has a total of 600 image samples with a dimension of 60, which are divided into 6 categories and reduced to the d' dimension. Then n=600, d=60, c=6, the sample matrix
[0105] ①Initialization which is
[0106] ②For the image data matrix XX T Perform eigendecomposition, and the eigenvectors corresponding to the first d′ largest eigenvalues form a matrix U, initialization
[0107] ③Use the following formula to update the matrix V
[0108]
[0109] ④ Use the following formula to update the row vector m j , thus updating the matrix M
[0110]
[0111] ⑤Update matrix Y
[0112] for each row vector y i , which are calculated sequentially
[0113]
[0114] build function
[0115]
[0116] where (x) + =max(0,x).
[0117]...
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