GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case

By Tianyi Zhou, Dacheng Tao. International Conference on Machine Learning, 2011

728 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f3a55c92fbbe068edce34441a37eeca3e700b043

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Citations in the corpus

On Compressing Deep Models by Low Rank and Sparse Decomposition (2017, methodology): “Testing accuracy (without retraining) comparison between the proposed method “GreBdec”, “GoDec” [31], SVD and pruning [8].”
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “…Zhao et al. (2014) [457] Bayesian-Ising-Signal (BIS) Huan et al. (2016) [158] Approximated RPCA GoDec Zhou and Tao (2011) [462] Semi-Soft GoDec Zhou and Tao (2011) [462] Sparse Additive Matrix Factorization SAMF Nakajima et al. (2012) [267] Variational Bayesian Sparse Estimator VBSE Chen et…”
Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017, methodology): ““go decomposition” (GoDec) [29] algorithm was used to enforce rank r and cardinality k constraints for the low-rank and sparse parts, respectively.”
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey (2024, unknown): “LQ-LoRA [126] uses an iterative algorithm inspired by robust principal components analysis [141], [142] which decomposes the weight W 0 such that W 0 « Q ` L 1 L 2 to resolve the inaccuracy caused by the quantization error, where Q is the quantized component which remains fixed and L 1 L 2 is the…”
A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection (2016, methodology): “The fourth data set is a HyMap image, which was captured at the location of the small town of Cook City, MT, USA, on July 4, 2006 [33], [34].”
Regularizing Neural Networks via Minimizing Hyperspherical Energy (2019, methodology): “More interestingly, we can also approximate W with a low-rank factorization [56]: W̃ = Y2(P1Y2) Y1∈R.”
Hyperspectral Image Restoration Using Low-Rank Matrix Recovery (2014, methodology): “D matrix, and the lowrank property of the image matrix is explored.”
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery (2017, methodology): “…faster online ( 0 ) Bilinear-dec[46] never works partial guarantee online ( 1 ) ORPCA [47] works well partial guarantee faster online ( 3 ) GoDec [37] works partial guarantee fast batch ( 3 ) pRoST [51] works no guarantee slower online ( 2 ) AltProj ReProCS-NORST assump’s, and memory: O ( nr log…”