Category label recovery method for low-rank image feature analysis
A technology of category labels and image features, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of model underfitting, missing labels, and low recognition rate.
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[0017] In order to make the present invention more obvious and understandable, the preferred embodiments are described in detail as follows in conjunction with the accompanying drawings:
[0018] Such as figure 1 As shown, first obtain all samples of the original sample set A, a total of n sample images, a total of C categories, in the c category there are N e samples, c ∈ {1, 2, ..., C}. The size of each image is S×S pixels, and the S×S-dimensional image matrix is pulled into an S 2 dimensional column vector, n images form a S 2 ×n-dimensional sample matrix x=[x 1 , x 2 ,...,x n ], the label y corresponding to each image 1 ,...y n ∈ {1, 2, ..., C}, corresponding to the original label matrix Y = [y 1 ,y 2 ,...,y n ]. For the above-mentioned original sample set A, the present invention provides a method for recovering class labels for low-rank image feature analysis, comprising the following steps:
[0019] Step 1: Generate u non-repetitive random numbers, and in ...
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