机构

• 自动化研究所 [3]
• 数学与系统科学研究院 [2]
• 新疆生态与地理研究所 [1]
• 长春光学精密机械与物... [1]
• 遥感与数字地球研究所 [1]
• 心理研究所 [1]
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• OAI收割 [11]

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• 期刊论文 [9]
• 会议论文 [1]
• 学位论文 [1]

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• 2023 [1]
• 2019 [1]
• 2016 [2]
• 2015 [2]
• 2014 [1]
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浏览/检索结果: 共11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提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models 期刊论文  OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 卷号: 33, 期号: 9, 页码: 4616-4629 作者:  Ma, Chengcheng;  Liu, Yang;  Deng, Jiankang;  Xie, Lingxi;  Dong, Weiming   |  收藏  |  浏览/下载：14/0  |  提交时间：2023/11/16 Vision-language model  prompt tuning  over-fitting  subspace learning  gradient projection   Time-varying state-space model identification of an on-orbit rigid-flexible coupling spacecraft using an improved predictor-based recursive subspace algorithm 期刊论文  OAI收割 Acta Astronautica, 2019, 卷号: 163, 页码: 157-167 作者:  Ni ZY(倪智宇);  Liu JG(刘金国);  Wu SN(邬树楠);  Wu ZG(吴志刚)   |  收藏  |  浏览/下载：27/0  |  提交时间：2018/12/01 Parameter identification  State-space model  Recursive subspace method  Time-varying system  Affine projection sign algorithm  State estimation   An orthogonally accumulated projection method for symmetric linear system of equations 期刊论文  OAI收割 SCIENCE CHINA-MATHEMATICS, 2016, 卷号: 59, 期号: 7, 页码: 1235-1248 作者:  Peng WuJian;  Lin Qun;  Zhang ShuHua   |  收藏  |  浏览/下载：25/0  |  提交时间：2018/07/30 iterative method  accumulated projection  conjugate gradient method  Krylov subspace   A Non-Krylov Subspace Method for Solving Large and Sparse Linear System of Equations 期刊论文  OAI收割 NUMERICAL MATHEMATICS-THEORY METHODS AND APPLICATIONS, 2016, 卷号: 9, 期号: 2, 页码: 289-314 作者:  Peng, Wujian;  Lin, Qun   |  收藏  |  浏览/下载：19/0  |  提交时间：2018/07/30 Iterative method  accumulated projection  Krylov subspace   Projection-Based NMF for Hyperspectral Unmixing 期刊论文  OAI收割 ieee journal of selected topics in applied earth observations and remote sensing, 2015, 卷号: 8, 期号: 6, 页码: 2632-2643 作者:  Yuan, Yuan;  Feng, Yachuang;  Lu, Xiaoqiang 收藏  |  浏览/下载：26/0  |  提交时间：2015/09/14 Hyperspectral unmixing  nonnegative matrix factorization (NMF)  spectral library  subspace projection   An Abundance Characteristic-Based Independent Component Analysis for Hyperspectral Unmixing 期刊论文  OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 卷号: 53, 期号: 1 作者:  Wang, Nan;  Du, Bo;  Zhang, Liangpei;  Zhang, Lifu 收藏  |  浏览/下载：28/0  |  提交时间：2016/04/20 Abundance characteristic  convex geometry  hyperspectral unmixing  independent component analysis (ICA)  orthogonal subspace projection   Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine 期刊论文  OAI收割 NEURAL PROCESSING LETTERS, 2014, 卷号: 39, 期号: 1, 页码: 25-43 作者:  Wang, Su-Jing;  Chen, Hui-Ling;  Yan, Wen-Jing;  Chen, Yu-Hsin;  Fu, Xiaolan 收藏  |  浏览/下载：25/0  |  提交时间：2015/09/18 Face recognition  Micro-expression recognition  Locality preserving projection  Discriminant information  Tensor subspace  Extreme learning machine   基于异常探测的高光谱端元提取方法研究/Endmember Extraction based on Anomaly Detection[J] 期刊论文  OAI收割 遥感技术与应用, 2011, 卷号: 26, 期号: 4, 页码: 457-460 作者:  彭青青;  杨辽;  王杰;  沈金祥;  中国科学院新疆生态与地理研究所,新疆乌鲁木齐830011／中国科学院研究生院,北京100049   |  收藏  |  浏览/下载：14/0  |  提交时间：2017/12/29 异常探测 端元提取 Rx算法 正交子空间投影 Anomaly Detection Endmember Extraction Rx Algorithm Orthogonal Subspace Projection Algorithm   子空间学习算法研究及其相关应用 学位论文  OAI收割 工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2010 陈伟 收藏  |  浏览/下载：93/0  |  提交时间：2015/09/02 子空间学习  二维线性判别分析  局部权重投影回归  PC-SURF算法  Subspace Learning  Two Dimensional Discriminant Analysis  Locallly Weighted Projection Regression  PC-SURF   Blind channel estimation of MIMO-OFDM based on FDPM (EI CONFERENCE) 会议论文  OAI收割 5th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2009, September 24, 2009 - September 26, 2009, Beijing, China Gang W.; Ming Z.; Hua C. 收藏  |  浏览/下载：17/0  |  提交时间：2013/03/25 Subspace based methods have been widely discussed in multichannel estimation including MIMO-OFDM system. However  direct subspace methods suffer from heavy computation complexity y of O(N3). In this paper  a new subspace tracking technique called Fast Data Projection Method (FDPM) is proposed for adaptively tracking the noise subspace. The new algorithm has low complexity of O(NL) and is the only numerically stable  low complexity algorithm for tracking subspaces corresponding to the smallest singular values. Simulation results show that the proposed scheme has similar performance to the original. 2009 IEEE.