|
|
|
|
|
Chowdhury Abrar Faiyaz, Pabel Shahrear, Rakibul Alam Shamim, Thilo Strauss and Taufiquar Khan
This paper aims to determine whether regularization improves image reconstruction in electrical impedance tomography (EIT) using a radial basis network. The primary purpose is to
investigate the effect of regularization to estimate the network parameters...
ver más
|
|
|
|
|
|
|
|
|
|
|
Dong Li and Lei Gong
Sensor alignment plays a key role in the accurate estimation of the ballistic trajectory. A sparse regularization-based sensor alignment method coupled with the selection of a regularization parameter is proposed in this paper. The sparse regularization ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yi Wang, Yating Xu, Tianjian Li, Tao Zhang and Jian Zou
Image deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed. For instance, convex sparse regularization tends to exhibit biased estimation, which can adversely impa...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yunkang Du and Zuoliang Xu
In this paper, we recover the European option volatility function σ(t)" role="presentation">??(??)s(t)
s
(
t
)
of the underlying asset and the fractional order α" role="presentation" style="position: relative;">??a
a
of the time fra...
ver más
|
|
|
|
|
|
|
|
|
|
|
Fansheng Zhang, Lianglin Dong, Hongbo Wang, Ke Zhong, Peiyuan Zhang and Jinyan Jiang
During the construction of underground engineering, the prediction of groundwater distribution and rock body permeability is essential for evaluating the safety of the project and guiding subsequent design and construction. This article proposes an objec...
ver más
|
|
|
|
|
|
|
|
|
|
|
Min Ma, Shanrong Liu, Shufei Wang and Shengnan Shi
Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal processin...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ryota Higashimoto, Soh Yoshida and Mitsuji Muneyasu
This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning an...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yugen Yi, Haoming Zhang, Ningyi Zhang, Wei Zhou, Xiaomei Huang, Gengsheng Xie and Caixia Zheng
As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature se...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yankai Lv, Haiyan Ding, Hao Wu, Yiji Zhao and Lei Zhang
Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally. Unlike traditional distributed optimization, FL trai...
ver más
|
|
|
|
|
|
|
|
|
|
|
Dozyslav B. Kuryliak and Oleksiy M. Sharabura
The problem of axially symmetric TM-wave diffraction from a bicone conjoined with an open-ended conical cavity is analysed rigorously. The scatterer is formed by the perfectly conducting semi-infinite and truncated semi-infinite conical surfaces; the sph...
ver más
|
|
|
|
|
|
|
|
|
|
|
Peng Chen and Huibing Wang
Semi-supervised metric learning intends to learn a distance function from the limited labeled data as well as a large amount of unlabeled data to better gauge the similarities of any two instances than using a general distance function. However, most exi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yejin Lee, Suho Lee and Sangheum Hwang
Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass dif...
ver más
|
|
|
|
|
|
|
|
|
|
|
Linghui Kong, Haizhong Qian, Yuqing Wu, Xinyu Niu, Di Wang and Zhekun Huang
Building outlines are important for emergency response, urban planning, and change analysis and can be quickly extracted from remote sensing images and raster maps using deep learning technology. However, such building outlines often have irregular bound...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guoqing Chen and Haizhong Qian
With the increasing availability of remote sensing images, the regularization of jagged building outlines extracted from high-resolution remote sensing images has become a current research hotspot. Based on an existing method proposed earlier by this aut...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shumin Lai, Longjun Huang, Ping Li, Zhenzhen Luo, Jianzhong Wang and Yugen Yi
In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve the p...
ver más
|
|
|
|
|
|
|
|
|
|
|
Rui-Yu Li, Yu Guo and Bin Zhang
Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized extens...
ver más
|
|
|
|
|
|
|
|
|
|
|
Rafael Díaz Fuentes, Stefano Serra-Capizzano and Rosita Luisa Sormani
It is well known that ω" role="presentation">???
?
-circulant matrices with ω≠0" role="presentation" style="position: relative;">???0??0
?
?
0
can be simultaneously diagonalized by a transform matrix, which can be factored...
ver más
|
|
|
|
|
|
|
|
|
|
|
Esmaeil Zahedi, Mohamad Saraee, Fatemeh Sadat Masoumi and Mohsen Yazdinejad
Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative and C...
ver más
|
|
|
|
|
|
|
|
|
|
|
Grigoriy Plusch, Sergey Arsenyev-Obraztsov and Olga Kochueva
|
|
|
|
|
|
|
|
|
|
|
Zhen Ye, Xiaoming Ou, Juhua Huang and Yingpin Chen
A traditional total variation (TV) model for infrared image deblurring amid salt-and-pepper noise produces a severe staircase effect. A TV model with low-order overlapping group sparsity (LOGS) suppresses this effect; however, it considers only the prior...
ver más
|
|
|
|
|
|