Results 111 to 120 of about 49,338 (223)

A self-training subspace clustering algorithm based on adaptive confidence for gene expression data. [PDF]

Front Genet, 2023
Li D, Liang H, Qin P, Wang J.
europepmc   +1 more source

Neural Collaborative Subspace Clustering

, 2019
We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces.
Harandi, Mehrtash, Huang, Wenbing, Ji, Pan, Li, Hongdong, Zhang, Tong   +4 more
core  

Greedy Subspace Clustering

, 2014
We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets to estimate the subspaces.
Park, Dohyung, Caramanis, Constantine, Sanghavi, Sujay   +2 more
openaire   +2 more sources

Boosted unsupervised feature selection for tumor gene expression profiles

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi, Kaixiang Yang, Mengzhi Wang, Zhiwen Yu, Huanqiang Zeng, Yang Hu   +5 more
wiley   +1 more source

Sparse and Low-Rank Subspace Data Clustering with Manifold Regularization Learned by Local Linear Embedding

Applied Sciences, 2018
Data clustering is an important research topic in data mining and signal processing communications. In all the data clustering methods, the subspace spectral clustering methods based on self expression model, e.g., the Sparse Subspace Clustering (SSC ...
Ye Yang, Yongli Hu, Fei Wu
doaj   +1 more source

Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach. [PDF]

Front Immunol, 2022
Sundaram S, Kim EN, Jones GM, Sivagnanam S, Tripathi M, Miremadi A, Di Pietro M, Coussens LM, Fitzgerald RC, Chang YH, Zhuang L.   +10 more
europepmc   +1 more source

AI‐Powered Anomaly Detection for Secure Internet of Things (IoT): Optimising XGBoost and Deep Learning With Bayesian Optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network ...
Seong‐O Shim, Lal Hussain, Mohammed A. Alqarni, Faisal S. Alsubaei, Rasha Jamal Atwah   +4 more
wiley   +1 more source

An Improved Fast Computational Method for Vibration Characteristic Analysis of Dry‐Type Iron Core Reactor

High Voltage, EarlyView.
ABSTRACT Structural mechanics field simulation plays a critical role in the vibration characteristic analysis of electrical equipment. Existing structural field analysis of equipment based on numerical simulation faces challenges such as convergence difficulties and prolonged computational time, failing to meet the demand for real‐time prediction of ...
Wanqing Wang, Caibo Liao, Bang Liu, Kai Li, Wenyu Liu, Zhaoguo Li, Xianfa Tian   +6 more
wiley   +1 more source

Inner Speech in Motor Cortex: Laying the Foundation for Privacy‐Preserving Speech Neuroprostheses

Medicine Bulletin, EarlyView.
Chenzhitong Chen, Weiqi Wang, Xingyu Mou, Tingting Tang, Xinling Chen, Qian Wang, Xi Yang, Xiaosong Hu, Zheng Yang   +8 more
wiley   +1 more source

Recognition of Spatiotemporal Feature Images of Zero‐Sequence Voltage for Single‐Phase High‐Impedance Ground Faults Under Complex Conditions

High Voltage, EarlyView.
ABSTRACT Diagnosing high‐impedance ground faults (HIGFs) in distribution networks is extremely challenging because high transition resistance significantly reduces electrical signal strength and unpredictable initial fault phase angles coupled with asymmetric voltage disturbances often lead to misclassification.
Zhengyang Li, Yijia Cao, Chun Chen, Weiyu Wang, Yansha Li, Jinglu Li   +5 more
wiley   +1 more source