Chemometrics and Intelligent Laboratory Systems

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Unsupervised Continual Learning Via Pseudo Labels (2021, methodology): “For part 2), we compare our method using the updated model from last incremental step as feature extractor as illustrated in Algorithm 1 with i) Scratch: apply a scratch model with the same network architecture as feature extractor, ii) PCA: directly apply PCA algorithm [Wold et al., 1987] on input ”
Tensors for Data Mining and Data Fusion (2016, methodology): “The traditional way for doing so is an expectation maximization approach that alternates between estimating the missing values with the current model and updating the model [Kiers 1997; Tomasi and Bro 2005].”
Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “Density-Based Methods OD methods include Gaussian distribution (Altman & Bland, 2005; Leys et al., 2013), Mahalanobis distance (De Maesschalck et al., 2000), Gaussian mixtures (Yang et al., 2009), and Local outlier factor (LOF) (Breunig et al., 2000).”
Counterexample-Guided Data Augmentation (2018, methodology): “This is very similar to model order reduction using Principal Component Analysis (PCA [Wold et al., 1987]).”