On updating the singular value decomposition

By Chang-Wan Jeon, Hyoung-Joong Kim, Jang-Gyu Lee. 1996

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Citations in the corpus

The Ideal Continual Learner: An Agent That Never Forgets (2023, methodology): “An important remark there is that expansion-based methods can be traced back to Bunch & Nielsen (1978) who proposed a method for incremental SVD. • In Appendix G, as promised in Remark 5, we present a dual approach to continual matrix factorization, where the storage consumption shrinks over time.”
Fast low-rank modifications of the thin singular value decomposition (2006, background): “Bunch and Nielsen [4] and Gu and Eisenstat [5] developed updates based on symmetric eigenvalue updates of the data’s gram matrix.”
Incremental Eigenanalysis for Classification (1998, methodology): “Again, the SVD method was also more accurate in terms of eigenvalues.”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “Bunch and Nielsen [25] also treated the case how to update the svd, if a row or column of matrix S was added or deleted.”
Streaming PCA and Subspace Tracking: The Missing Data Case (2018, methodology): “In addition, “incremental SVD” [15] or “updating the SVD” [48] are terminology used more classically in numerical methods.”
Online Machine Learning in Big Data Streams (2018, background): “Online variants are based on ideas to incrementally update the matrix decomposition [37, 106, 36].”
Computer Vision - ECCV 2004 (2004, methodology): “Our model provides a unified view of two previous lines of research: it may be thought as a probabilistic interpretation of David Lowe’s work [7,8] or, conversely, as a special case of the constellation model [4] where many of the parameters are pooled amongst models, rather than learned individuall”
Online Kernel Principal Component Analysis: A Reduced-Order Model (2012, methodology): “To address this drawback, different incremental techniques have been proposed, such as [2], [3], or more recently [4] (and references therein), however still with high computational cost.”
Auto-association by multilayer perceptrons and singular value decomposition (1988, background): “However, the SVD algorithm has also a sequential version (Bunch and Nielsen 1978) and the argument therefore does not apply.”