Sparse Table is a data structure, that allows answering range queries. It can answer range minimum queries (or equivalent range maximum queries) in O(1) time, and another queries in O(log n). You can read how to
Demeulenaere, J., Hartert, R., Lecoutre, C., Perez, G., Perron, L., R麓egin, J., Schaus, P.: Compact-table: Efficiently filtering table constraints with reversible sparse bit-sets. In: Rueher, M. (ed.) Principles and Practice of Constraint Pro- gramming (CP 2016). LNCS, ...
Table 4 summarizes the comparison results. Obviously, unlike PCA, the three sparse PCA algorithms have the correct sparse representations Sparse Unsupervised Dimensionality Reduction Algorithms 369 Table 3. The first six PCs obtained by sPCA-OS on the pitprops dataset Variable topdiam length moist ...
arXiv:2501.03776v1 [math.NA] 7 Jan 2025Group Sparse-based Tensor CP Decomposition: Model, Algorithms,and Applications in ChemometricsZihao Wang ∗ , Minru Bai † , Liang Chen ‡ , and Xueying Zhao §January 8, 2025AbstractThe CANDECOMP/PARAFAC (or Canonical polyadic, CP) decomposition o...
(that is, within 1–2 Mb). While several methods have been developed to model and correct known sources of Hi-C biases explicitly with joint functions, the most commonly used strategy is to ‘normalize’ the Hi-C matrices and correct Hi-C biases implicitly with matrix-balancing algorithms...
In the new user problem, users have only rated a small number of items, which is insufficient for recommender systems to provide accurate personalized recommendations and introduces a major contradiction and difficulty when we design recommendation algorithms. The procedure of user-based CF is ...
these techniques are not based on incomplete traditional assumptions. Matrix decomposition is the principal idea behind these algorithms and it is assumed that clutter and blood signals lie in different subspaces. Therefore, eigen-based filters are considered adaptive to gross motions induced by the son...
Table 3 shows the estimated rank for each sub-tensor by each algorithm in comparison with the true rank R̃. Algorithms SeekAndDestroy and NLS allow to determine the correct rank R̃ for some sub-tensors but tend to underestimated the rank value for most sub-tensors. Conversely, R-CPD...
Moreover, integrating advanced sensor technologies and machine learning algorithms has significantly enhanced structural health monitoring (SHM) for bridges. Despite being increasingly used in traditional SHM applications, studies using autoencoders within drive-by methodologies are rare, especially in the ...
ESSEX: Equipping Sparse Solvers For Exascale 157 3.2.4 A Posteriori Cross-Interval Orthogonalization Rayleigh–Ritz-based subspace iteration algorithms naturally produce a B- orthogonal set of eigenvectors X, i.e., orth(X) is small, where orth(X) = max orth(xi, xj )|i = j with orth(x,...