An L1-and-L2-Norm-Oriented Latent Factor Model for Recommender Systems
A recommender system (RS) is highly efficient in filtering people's desired information from
high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach …
high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach …
Learning a low tensor-train rank representation for hyperspectral image super-resolution
Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
Decentralized Rank-Adaptive Matrix Factorization—Part I: Algorithm Development
Factorizing a low-rank matrix into two matrix factors with low dimensions from its noisy
observations is a classical but challenging problem arising from real-world applications. This …
observations is a classical but challenging problem arising from real-world applications. This …
Bayesian low rank tensor ring for image recovery
Low rank tensor ring based data recovery can recover missing image entries in signal
acquisition and transformation. The recently proposed tensor ring (TR) based completion …
acquisition and transformation. The recently proposed tensor ring (TR) based completion …
Fault detection for dynamic processes based on recursive innovational component statistical analysis
Fault detection has long been a hot research issue for industry. Many common algorithms
such as principal component analysis, recursive transformed component statistical analysis …
such as principal component analysis, recursive transformed component statistical analysis …
Robust low-rank latent feature analysis for spatiotemporal signal recovery
Wireless sensor network (WSN) is an emerging and promising developing area in the
intelligent sensing field. Due to various factors like sudden sensors breakdown or saving …
intelligent sensing field. Due to various factors like sudden sensors breakdown or saving …
Feature extraction for incomplete data via low-rank tensor decomposition with feature regularization
Multidimensional data (ie, tensors) with missing entries are common in practice. Extracting
features from incomplete tensors is an important yet challenging problem in many fields …
features from incomplete tensors is an important yet challenging problem in many fields …
Learning Optimized Structure of Neural Networks by Hidden Node Pruning With Regularization
X Xie, H Zhang, J Wang, Q Chang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
We propose three different methods to determine the optimal number of hidden nodes
based on L 1 regularization for a multilayer perceptron network. The first two methods …
based on L 1 regularization for a multilayer perceptron network. The first two methods …
Joint embedding learning and low-rank approximation: A framework for incomplete multiview learning
H Tao, C Hou, D Yi, J Zhu, D Hu - IEEE transactions on …, 2019 - ieeexplore.ieee.org
In real-world applications, not all instances in the multiview data are fully represented. To
deal with incomplete data, incomplete multiview learning (IML) rises. In this article, we …
deal with incomplete data, incomplete multiview learning (IML) rises. In this article, we …
Uncertainty-adjusted recommendation via matrix factorization with weighted losses
In a recommender systems (RSs) dataset, observed ratings are subject to unequal amounts
of noise. Some users might be consistently more conscientious in choosing the ratings they …
of noise. Some users might be consistently more conscientious in choosing the ratings they …