非组织政治活动robust tensor princel analysis
friends Robust tensor principal component analysis (rTPCA) is a technique ud for dealing with large data matrices or tensors that contain outliers or corrupted data. It involves decomposing a data tensor into a low-rank matrix of underlying patterns and a spar matrix of noi/outliers.
sgp The algorithm works by minimizing a cost function that includes the Frobenius norm of the low-rank and spar components, along with a penalty term that encourages sparsity in the latter. The optimization problem is solved using iterative convex optimization techniques, such as the alternating direction method of multipliers (ADMM).口语考试
adwin
oul>专业英语翻译 Compared to traditional PCA, which assumes that the input data is clean and error-free, rTPCA is more resilient to outliers and can handle data with missing values. It has been ud in various applications such as image and video processing, bioinformatics, and finance.
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