- Referenced in 707 articles
- structure are exploited. We also exploit low-rank structures in the constraint matrices associated...
- Referenced in 272 articles
- Expansin pack: Special-purpose rank-revealing algorithms. This collection of Matlab 7.0 software supplements ... includes implementations of special-purpose rank-revealing algorithms developed since the publication of the original ... provide algorithms for computing and modifying symmetric rank-revealing VSV decompositions, we expand the algorithms ... handle interference-type problems with a rank-deficient covariance matrix, and we provide a robust...
- Referenced in 268 articles
- problem of learning a ranking function. The optimization algorithms used in SVMlight are described ... this version is an algorithm for learning ranking functions [Joachims, 2002c]. The goal ... objects as accurately as possible. Such ranking problems naturally occur in applications like search engines...
- Referenced in 421 articles
- Cholesky factorization, matrix inversion, full-rank linear least squares problems, orthogonal and generalized orthogonal factorizations...
- Referenced in 148 articles
- based on the idea of low-rank factorization. A specialized version of SDPLR is also ... Programming Algorithm for Semidefinite Programs via Low-rank Factorization” written by S. Burer and R.D.C...
- Referenced in 138 articles
- nets, and how to merge these sub-rankings derived from the chain CP-nets ... generate the preference ranking of the tree-structured CP-net. At last, the corresponding analysis...
- Referenced in 186 articles
- expressed as the sum of rank-1 tensors. We are interested in the case where...
- Referenced in 112 articles
- Ordinal regression revisited: multiple criteria ranking with a set of additive value functions. VisualUTA ... method for multiple criteria ranking of alternatives from set A using a set of additive ... necessary (strong) and a possible (weak) ranking of alternatives from A, being, respectively, a partial...
- Referenced in 125 articles
- manifolds is well-suited to deal with rank and orthogonality constraints. Such structured constraints appear ... pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network...
- Referenced in 110 articles
- additive models, and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Reduced-Rank VGAMs). This...
- Referenced in 134 articles
- Rangiranje (VIKOR), Data Envelopment Analysis (DEA), Preference Ranking METHod for Enrichment Evaluations (PROMETHEE), ELimination...
- Referenced in 130 articles
- according to the three main problematics: choosing, ranking and sorting. The fourth section presents...
- Referenced in 124 articles
- underlying objective function are made. Only the ranking between candidate solutions is exploited for learning...
- Referenced in 77 articles
- block term decompositions (BTD) and low multilinear rank approximation (LMLRA), complex optimization: quasi-Newton ... cumulants, tensor visualization, estimating a tensor’s rank or multilinear rank...
- Referenced in 119 articles
- package FRK. Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large...
- Referenced in 107 articles
- than those assumed in simplex implementations. Severe rank deficiency must also be accommodated, making...
- Referenced in 88 articles
- values are important, but rather that the rankings of solutions induced by the hypervolume indicator...
- Referenced in 63 articles
- solve large scale SDPs arising from rank-1 tensor approximation problems constructed ... largest rank-1 tensor approximation problem we solved (in about 14.5 h) is nonsym...
- Referenced in 85 articles
- inverses or known eigenvalues; ill-conditioned or rank deficient matrices; and symmetric, positive definite, orthogonal...
- Referenced in 83 articles
- columns or both, and for computing low-rank SVDs on large sparse centered matrices...