Results 81 to 90 of about 13,997,719 (325)
Nonconvex Low Tubal Rank Tensor Minimization
In the sparse vector recovery problem, the L0-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a ...
Yaru Su, Xiaohui Wu, Genggeng Liu
doaj +1 more source
Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications
Abstract In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or removed. This problem holds practical importance in model selection, such as cross-validation (CV) and feature selection.
Hanada, Hiroyuki +3 more
openaire +4 more sources
Nonconvex Robust Low-Rank Matrix Recovery [PDF]
In this paper we study the problem of recovering a low-rank matrix from a number of random linear measurements that are corrupted by outliers taking arbitrary values. We consider a nonsmooth nonconvex formulation of the problem, in which we explicitly enforce the low-rank property of the solution by using a factored representation of the matrix ...
Li, Xiao +3 more
openaire +2 more sources
ABSTRACT Purpose Cognitive and psychological difficulties could negatively interfere with treatment adherence and quality of life before and after hematopoietic stem cell transplant (HSCT). Methods to mitigate these changes may have positive effects on treatment success.
Kristen L. Votruba +11 more
wiley +1 more source
Dynamic modeling of exhaust emission from a 600 MW once-through reheat down-fired boiler
The increasing integration of clean energy sources into the grid has required coal-fired power plants to frequently adjust their output to keep up with demand.
Chen Han +8 more
doaj +1 more source
Probabilistic Low-Rank Multitask Learning
In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task.
Yu Kong, Ming Shao, Kang Li, Yun Fu
openaire +2 more sources
ABSTRACT A second allogeneic (allo‐)hematopoietic stem cell transplantation (HSCT2) is a potential curative option for pediatric patients with acute lymphoblastic leukemia (ALL) following relapse after first allogeneic transplantation (HSCT1), but its efficacy is limited by high relapse rates and transplant‐related toxicity in highly pretreated ...
Ava Momm +10 more
wiley +1 more source
ABSTRACT Background/Objectives Outcomes for pediatric relapsed/refractory (R/R) acute myeloid leukemia (AML) remain dismal. CPX‐351, a liposomal formulation of cytarabine and daunorubicin, may have less off‐target toxicities than traditional chemotherapies and has shown improved outcomes for adults with newly diagnosed therapy‐related AML.
Jonathan D. Bender +17 more
wiley +1 more source
Hyperspectral anomaly detection is an important unsupervised binary classification problem that aims to effectively distinguish between background and anomalies in hyperspectral images (HSIs).
Xi’ai Chen +5 more
doaj +1 more source
Correlation Clustering with Low-Rank Matrices
Correlation clustering is a technique for aggregating data based on qualitative information about which pairs of objects are labeled 'similar' or 'dissimilar.' Because the optimization problem is NP-hard, much of the previous literature focuses on ...
Arthur D. +7 more
core +1 more source

