robust tensor princel analysis
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.
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).
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.
