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Decreasing norm-trace codes
The decreasing norm-trace codes are evaluation codes defined by a set of monomials closed under divisibility and the rational points of the extended...
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Comparison of Matrix Norm Sparsification
A well-known approach in the design of efficient algorithms, called matrix sparsification, approximates a matrix A with a sparse matrix
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The seven troubles with norm-compliant robots
Many researchers from robotics, machine ethics, and adjacent fields seem to assume that norms represent good behavior that social robots should learn...
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Capped norm linear discriminant analysis and its applications
Classical linear discriminant analysis (LDA) is based on squared Frobenious norm and hence is sensitive to outliers and noise. To improve the...
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Infrared small target detection based on Bi-Nuclear norm minimization
Infrared small target detection (ISTD) in complex backgrounds poses significant challenges in modern applications. Existing solutions based on...
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A general multi-factor norm based low-rank tensor completion framework
Low-rank tensor completion aims to recover the missing entries of the tensor from its partially observed data by using the low-rank property of the...
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A multi-scenario approach to continuously learn and understand norm violations
Using norms to guide and coordinate interactions has gained tremendous attention in the multiagent community. However, new challenges arise as the...
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Truncated nuclear norm matrix recover algorithm for direction-of-arrival estimation
The received signal may suffer from continuous missing data due to reasons such as antenna damage, which affects the performance of the...
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Weighted hybrid truncated norm regularization method for low-rank matrix completion
Matrix completion is usually formulated as a low-rank matrix approximation problem. Several methods have been proposed to solve this problem, e.g.,...
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Upper triangulation-based infinity norm bounds for the inverse of Nekrasov matrices with applications
The infinity norm bounds for the inverse of Nekrasov matrices play an important role in scientific computing. We in this paper propose a...
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Multi-view clustering via dual-norm and HSIC
Fully capturing valid complementary information in multi-view data enhances the connection between similar data points and weakens the correlation...
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Lq-norm multiple kernel fusion regression for self-cleansing sediment transport
Experimental and modeling studies have been conducted to develop an approach for self-cleansing rigid boundary open channel design such as drainage...
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A robust low-rank matrix completion based on truncated nuclear norm and Lp-norm
The low-rank matrix completion problem has aroused notable attention in various fields, such as engineering and applied sciences. The classical...
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Self-supervised deep subspace clustering with entropy-norm
Auto-Encoder based Deep Subspace Clustering (DSC) has been widely applied in computer vision, motion segmentation and image processing. However,...
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Data-driven analysis and prediction of norm acceptance
That norms matter for politics is a widely shared observation. Existing political science research on norm diffusion, norm localization, and...
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Towards Norm Entrepreneurship in Agent Societies
One of the principal focuses of normative multi-agent systems in a distributed environment is coordination among agents. Most work in this area has...
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Efficient Bayesian CNN Model Compression using Bayes by Backprop and L1-Norm Regularization
The swift advancement of convolutional neural networks (CNNs) in numerous real-world utilizations urges an elevation in computational cost along with...
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Zero-Norm ELM with Non-convex Quadratic Loss Function for Sparse and Robust Regression
Extreme learning machine (ELM) is a machine learning technique with simple structure, fast learning speed, and excellent generalization ability,...
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Irıs Predıctıon Usıng L1-Norm-Based Convolutıonal Network Model Convolutıonal Network Model
In biometric identification, iris recognition is a non-contact identification method with higher significance. The images used for validation are...
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Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised classification
For semi-supervised learning, a few labeled data and a large number of unlabeled data are used to construct a reasonable classifier. In recent years,...
