Suchergebnisse - rank factorization algorithms

Decentralized Rank-Adaptive Matrix Factorization-Part I: Algorithm Development von Jiao, Yuchen, Gu, Yuantao, Chang, Tsung-Hui, Luo, Zhi-Quan

“… This paper develops decentralized matrix factorization algorithms, i.e., factorizing a matrix whose columns are stored distributively over a network without a central agent …”
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Efficient algorithms for computing rank‐revealing factorizations on a GPU von Heavner, Nathan, Chen, Chao, Gopal, Abinand, Martinsson, Per‐Gunnar

“… This article presents two alternative algorithms for computing a rank‐revealing factorization of the form , where and are orthogonal and is trapezoidal …”
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Low Rank Matrix Factorization Algorithm Based on Multi-Graph Regularization for Detecting Drug-Disease Association von Ai, Chengwei, Yang, Hongpeng, Ding, Yijie, Tang, Jijun, Guo, Fei

“… On the basis of low-rank matrix factorization with <inline-formula><tex-math notation="LaTeX">L_{2}</tex-math> <mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic …”
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Cine Cone Beam CT Reconstruction Using Low-Rank Matrix Factorization: Algorithm and a Proof-of-Principle Study von Jian-Feng Cai, Xun Jia, Hao Gao, Jiang, Steve B., Zuowei Shen, Hongkai Zhao

“… Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over …”
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Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications von Haeffele, Benjamin D., Vidal, Rene

“… Convex formulations of low-rank matrix factorization problems have received considerable attention in machine learning …”
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Adaptive Rank Estimation Based Tensor Factorization Algorithm for Low-Rank Tensor Completion von Liu, Han, Liu, Jing, Su, Liyu

“… However, tubal rank of the original tensor is assumed known in TCTF method. In this paper, we propose an adaptive rank estimation based tensor factorization (ARE-TF …”
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An algorithm for low-rank matrix factorization and its applications von Chen, Baiyu, Yang, Zi, Yang, Zhouwang

“… This paper proposes a valid and fast algorithm for low-rank matrix factorization …”
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Efficient algorithm for low-rank matrix factorization with missing components and performance comparison of latest algorithms von Okatani, T., Yoshida, T., Deguchi, K.

“… This paper examines numerical algorithms for factorization of a low-rank matrix with missing components …”
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Randomized Algorithms for Low-Rank Matrix Factorizations: Sharp Performance Bounds von Witten, Rafi, Candès, Emmanuel

“… The development of randomized algorithms for numerical linear algebra, e.g. for computing approximate QR and SVD factorizations, has recently become an intense area of research …”
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Double feature selection algorithm based on low-rank sparse non-negative matrix factorization von Shang, Ronghua, Song, Jiuzheng, Jiao, Licheng, Li, Yangyang

“… To this end, this paper proposes a new feature selection algorithm called double feature selection algorithm based on low-rank sparse non-negative matrix factorization (NMF-LRSR …”
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Low-Rank Factorizations in Data Sparse Hierarchical Algorithms for Preconditioning Symmetric Positive Definite Matrices von Agullo, Emmanuel, Darve, Eric, Giraud, Luc, Harness, Yuval

“… We consider the problem of choosing low-rank factorizations in data sparse matrix approximations for preconditioning large-scale symmetric positive definite (SPD) matrices …”
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A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization von Burer, Samuel, Monteiro, Renato D.C.

“… The algorithm's distinguishing feature is a change of variables that replaces the symmetric, positive semidefinite variable X of the SDP with a rectangular variable R according to the factorization X=RRT …”
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Algorithms and Applications to Weighted Rank-one Binary Matrix Factorization von Lu, Haibing, Chen, X I, Shi, Junmin, Vaidya, Jaideep, Atluri, Vijayalakshmi, Hong, Yuan, Huang, Wei

“… Matrix factorization methods have been widely used tools for the analysis of high-dimensional data, as they automatically extract sparse and meaningful features from data vectors …”
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Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm von Wen, Zaiwen, Yin, Wotao, Zhang, Yin

“… —a task that is increasingly costly as matrix sizes and ranks increase. To improve the capacity of solving large-scale problems, we propose a low-rank factorization model and construct a nonlinear successive over-relaxation (SOR …”
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A hybrid algorithm for low-rank approximation of nonnegative matrix factorization von Wang, Peitao, He, Zhaoshui, Xie, Kan, Gao, Junbin, Antolovich, Michael, Tan, Beihai

“… The new algorithm converges very rapidly. Then, we propose a hybrid NMF (HNMF) algorithm that combines the new algorithm with the low-rank approximation based NMF (lraNMF) algorithm …”
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Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation von Zhu, Xiaoke, Jing, Xiao-Yuan, Wu, Di, He, Zhenyu, Cao, Jicheng, Yue, Dong, Wang, Lina

“… Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much …”
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Deviation maximization for rank-revealing QR factorizations von Dessole, Monica, Marcuzzi, Fabio

“… ”, and apply it to compute rank-revealing QR factorizations as an alternative to the well-known block version of the QR factorization with the column pivoting method, called QP3 and currently implemented in LAPACK’s xgeqp3 routine …”
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Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization von Gu, Ming, Eisenstat, Stanley C.

“… $ such that the QR factorization \[ M\Pi = Q\left( {\begin{array}{*{20}c} {A_k } & {B_k } \\ {} & {C_k } \\ \end{array} } \right) \] reveals the numerical rank …”
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Computing rank‐revealing factorizations of matrices stored out‐of‐core von Heavner, N., Martinsson, P. G., Quintana‐Ortí, G.

“… This paper describes efficient algorithms for computing rank‐revealing factorizations of matrices that are too large to fit in main memory (RAM …”
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Text feature selection algorithm based on Chi-square rank correlation factorization von Li, Yan-Hong

“… Therefore, a text feature selection algorithm based on Chi-square rank correlation factorization is proposed based on the comprehensive consideration of the whole and local distribution of text features …”
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