机构

• 沈阳自动化研究所 [3]
• 数学与系统科学研究院 [2]
• 西安光学精密机械研究... [2]
• 计算技术研究所 [1]
• 国家空间科学中心 [1]
• 长春光学精密机械与物... [1]
• 更多

采集方式

• OAI收割 [12]

内容类型

• 期刊论文 [9]
• 会议论文 [3]

发表日期

• 2021 [2]
• 2020 [1]
• 2019 [2]
• 2018 [1]
• 2017 [2]
• 2012 [3]
• 更多

学科主题

• 空间技术 [1]

筛选

浏览/检索结果: 共12条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 A survey and systematic assessment of computational methods for drug response prediction 期刊论文  OAI收割 BRIEFINGS IN BIOINFORMATICS, 2021, 卷号: 22, 期号: 1, 页码: 232-246 作者:  Chen, Jinyu;  Zhang, Louxin   |  收藏  |  浏览/下载：38/0  |  提交时间：2021/06/01 Drug response prediction  regression model  low rank matrix factorization based approach  Bayesian inference  deep learning  benchmark   Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model 期刊论文  OAI收割 JOURNAL OF MACHINE LEARNING RESEARCH, 2021, 卷号: 22, 页码: 39 作者:  Hao, Meiling;  Qu, Lianqiang;  Kong, Dehan;  Sun, Liuquan;  Zhu, Hongtu   |  收藏  |  浏览/下载：45/0  |  提交时间：2021/10/26 High dimension  Imaging genetics  Matrix linear regression  Optimal mini-max rate  Variable selection   Trust-embedded collaborative deep generative model for social recommendation 期刊论文  OAI收割 JOURNAL OF SUPERCOMPUTING, 2020, 页码: 29 作者:  Deng, Xiaoyi;  Wu, Yenchun Jim;  Zhuang, Fuzhen   |  收藏  |  浏览/下载：30/0  |  提交时间：2020/12/10 Recommender system  Deep generative model  Collaborative topic regression  Trust matrix factorization  Deep learning   An efficient framework for unsupervised feature selection 期刊论文  OAI收割 NEUROCOMPUTING, 2019, 卷号: 366, 页码: 194-207 作者:  Zhang, Han;  Zhang, Rui;  Nie, Feiping;  Li, Xuelong   |  收藏  |  浏览/下载：103/0  |  提交时间：2019/10/18 Efficient unsupervised feature selection  Bipartite graph  Discrete indicator matrix  Uncorrelated regression model   Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation 期刊论文  OAI收割 IEEE Transactions on Neural Networks and Learning Systems, 2019, 卷号: 30, 期号: 2, 页码: 474-485 作者:  Chang, Xiangyu;  Zhong, Yan;  Lin SB(林绍波);  Wang Y(王尧)   |  收藏  |  浏览/下载：37/0  |  提交时间：2018/07/31 Degrees of freedom  low-rank matrix estimate  multivariate linear regression  multivariate quantile regression (QR)   Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation 期刊论文  OAI收割 IEEE Transactions on Neural Networks and Learning Systems, 2018, 页码: 1-12 作者:  Wang Y(王尧)   |  收藏  |  浏览/下载：20/0  |  提交时间：2018/07/31 Degrees of freedom  low-rank matrix estimate  multivariate linear regression  multivariate quantile regression (QR)   Refined-Graph Regularization-Based Nonnegative Matrix Factorization 期刊论文  OAI收割 ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2017, 卷号: 9, 期号: 1 作者:  Li, Xuelong;  Cui, Guosheng;  Dong, Yongsheng;  Dong, Yongsheng (dongyongsheng98@163.com)   |  收藏  |  浏览/下载：41/0  |  提交时间：2017/09/26 Data Representation  Refined-graph  Nonnegative Matrix Factorization (Nmf)  Least Squares Regression  Image Clustering   Ordinal regression based on learning vector quantization 期刊论文  OAI收割 Neural Networks, 2017, 卷号: 93, 页码: 76-88 作者:  Tang FZ(唐凤珍);  Tio, Peter   |  收藏  |  浏览/下载：32/0  |  提交时间：2017/06/20 Ordinal regression  Learning vector quantization  Generalized matrix learning vector quantization   Image deblurring with matrix regression and gradient evolution 期刊论文  OAI收割 PATTERN RECOGNITION, 2012, 卷号: 45, 期号: 6, 页码: 2164-2179 作者:  Xiang, Shiming;  Meng, Gaofeng;  Wang, Ying;  Pan, Chunhong;  Zhang, Changshui 收藏  |  浏览/下载：18/0  |  提交时间：2015/08/12 Image deblurring  Interactive deblurring  Matrix regression  Gradient evolution  Supervised learning   Image quality assessment based on gradient complex matrix (EI CONFERENCE) 会议论文  OAI收割 2012 International Conference on Systems and Informatics, ICSAI 2012, May 19, 2012 - May 20, 2012, Yantai, China 作者:  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：26/0  |  提交时间：2013/03/25 An image quality assessment model based on gradient complex matrix is proposed. The vertical and horizontal gradient information of grayscale image is calculated. Complex number is used to construct the measuring matrix. Singular value decomposition is performed in order to obtain the main structure information of the image. The singular value feature vectors of the image gradient complex matrices corresponding to the reference image and the distorted image are used to measure the structural similarity of the two images. PSNR is taken as a tool to evaluate the gradient distribution similarity. Their properties are analyzed by using LIVE database and nonlinearity regression function. 2012 IEEE.