Sparse reinforcement type low-rank constraint face image clustering method
A low-rank constraint, face image technology, applied in the field of face image clustering, can solve the problems of invalid feature interference, high computational complexity, low discrimination of local information, etc., to achieve high accuracy, strong data adaptability, The effect of high operating efficiency
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[0021] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.
[0022] A face image clustering method with sparse enhanced low-rank constraints, including using sparse representation to represent high-dimensional features in image data with low rank, using feature weights to measure the relative contribution of different features, and using regularized non-convex penalties The function constraint represents the coefficient matrix, enhances the accuracy of its singular value, and finally improves the accuracy of the target feature extraction by improving the clustering ability of the local information of the subspace. Including the following steps:
[0023] Step 1, weighted feature evaluation of the sparse representation, ensures that the reconstructed representation is enforced by efficient features. In reality, affected by the invalid features of high-dimensional data, the distribution of the error term ...
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