Sequential Karhunen-Loeve basis extraction and its application to images

By Avraham Levy, M. Lindenbaum. 1998

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One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares (2022, positive): “The main idea is to summarize the orthogonal basis S using an incremental principal component analysis (IPCA) algorithm, known as the sequential Karhunen–Loeve (SKL) algorithm proposed in [11].”
Incremental Evolving Domain Adaptation (2016, methodology): “Since data arrive sequentially, the lower dimensional target subspace is changed incrementally and this subspace is found by a variant of the sequential Karhunen-Loeve method introduced in [17].”
Fast low-rank modifications of the thin singular value decomposition (2006, methodology): “The Levy and Lindenbaum [7] method takes linear time for r min(p, q) but appears to exhibit quadratic scaling well before r = √p.”
Incremental Singular Value Decomposition of Uncertain Data with Missing Values (2002, methodology): “Levy and Lindenman [14] exploit the relationship between the QR -decomposition and the SVD to incrementally compute the left singular vectors in O ( pqr 2 ) time; if p , q , and r are known in advance and p (cid:29) q (cid:29) r , then the expected complexity falls to O ( pqr ) .”
A tutorial overview of anomaly detection in hyperspectral images (2010, methodology): “Howev er, the SVD step can be carried out in a sequential manner [ 45 ], thus speeding up the computat ion.”