作者机构：
[付丽华; 黄娟; 李宏伟] School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China;[张猛] Department of Computer Science, Central China Normal University, Wuhan 430079, China
通讯机构：
School of Mathematics and Physics, China University of Geosciences, China
作者机构：
[Li, Hongwei; Fu, Lihua] School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China;[Zhang, Meng] Department of Computer Science, Central China Normal University, 152 Luoyu Road, Wuhan 430079, China
通讯机构：
Department of Computer Science, Central China Normal University, 152 Luoyu Road, China
期刊：
Digital Signal Processing,2008年18(4):534-542 ISSN：1051-2004
通讯作者：
Fu, Lihua
作者机构：
[Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Hubei, Peoples R China.;[Fu, Lihua; Li, Hongwei] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Hubei, Peoples R China.;[Wang, Gaofeng] Wuhan Univ, CJ Huang Informat Technol Res Inst, Wuhan 430072, Hubei, Peoples R China.;[Wang, Gaofeng] Siargo Inc, Cupertino, CA 95014 USA.
通讯机构：
[Fu, Lihua] C;China Univ Geosci, Sch Math & Phys, Wuhan 430074, Hubei, Peoples R China.
关键词：
Harmonic retrieval;Multiplicative and additive noises;Normalized scalogram;Wavelet transform
摘要：
A novel approach for frequency estimation of one-dimensional harmonics in multiplicative and additive noises is presented. To overcome the resolution limitation inherent to the traditional Fourier-based algorithms, a wavelet transform is utilized. In this new approach, we use a wavelet mother function with a tunable parameter, which is constructed by modulating a window function. For a given harmonic retrieval problem, the tunable parameter can be adaptively adjusted to achieve a good performance. Some numerical experiments are included to illustrate the merits of this new approach.
期刊：
IEEE Signal Processing Letters,2008年15(1):653-656 ISSN：1070-9908
通讯作者：
Zhang, Meng
作者机构：
[He, Tingting; Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Fu, Lihua] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Wang, Gaofeng] Wuhan Univ, CJ Huang Informat Technol Res Inst, Wuhan 430072, Peoples R China.;[Wang, Gaofeng] Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Peoples R China.
通讯机构：
[Zhang, Meng] C;Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
关键词：
least squares approximations;regression analysis;tree searching;orthogonal least-squares regression;tree structure search algorithm;Orthogonal least squares;repeating weighted boosting search;tree structure search
摘要：
Orthogonal least-squares (OLS) regression with tunable kernels has been recently introduced, in which a greedy scheme is utilized to tune the parameters of each individual regressor term by term using a global search algorithm. To improve the performance of the greedy-scheme-based OLS algorithm, a tree structure search algorithm is constructed. At each regressor stage, this proposed OLS algorithm is realized by keeping multiple best regressors rather than using the optimal one only. Numerical results show that this new scheme is capable of producing a much sparser regression model with better generalization than the conventional approaches.
期刊：
International Journal of Business Intelligence and Data Mining,2008年3(4):437-450 ISSN：1743-8187
通讯作者：
Zhang, Meng
作者机构：
[He, Tingting; Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Zhou, Jiaogen] Wuhan Univ, Digital Engn Ctr, Wuhan 430072, Peoples R China.;[Fu, Lihua] China Univ Geosci, Sch Math & Phys, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, Meng] C;Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
会议名称：
4th International Conference on Fuzzy Systems and Knowledge Discovery
会议时间：
AUG 24-27, 2007
会议地点：
Haikou, PEOPLES R CHINA
会议主办单位：
[Zhang, Meng;He, Tingting] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.^[Zhou, Jiaogen] Wuhan Univ, Digital Engn Ctr, Wuhan 430072, Peoples R China.^[Fu, Lihua] China Univ Geosci, Sch Math & Phys, Wuhan 430079, Peoples R China.
摘要：
This paper considers sparse regression modelling using a generalised kernel model in which each kernel regressor has its individually tuned centre vector and diagonal covariance matrix. An Orthogonal Least Squares (OLS) forward selection procedure is employed to select the regressors one by one using a guided random search algorithm. In order to prevent the possible overfitting, a practical method to select the termination threshold is used. A novel hybrid wavelet is constructed to make the model sparser. The experimental results show that this generalised model outperforms the traditional methods in terms of precision and sparseness. The model with the wavelet and hybrid kernel has a much faster convergence rate compared to that with a conventional Radial Basis Function (RBF) kernel.
期刊：
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 3, PROCEEDINGS,2007年:568-+
通讯作者：
Zhou, JG
作者机构：
[Zhou, Jiaogen; Bian, Fuling] Wuhan Univ, Spatial Informat & Digital Engn Ctr, Wuhan 430079, Peoples R China.;[Guan, Jihong] Tongji Univ, Dept Comp Sci & Technol, Shanghai 200092, Peoples R China.;[Zhang, Meng] Huazhong Normal Univ, Dept Comp Sci & Technol, Wuhan 430079, Peoples R China.
通讯机构：
[Zhou, JG] W;Wuhan Univ, Spatial Informat & Digital Engn Ctr, Wuhan 430079, Peoples R China.
摘要：
Many applications scenarios require spatial clustering results in which a cluster has not only high proximity in geometrical domain but also high similarity in non-geometrical domain. Such clustering problem is called dual clustering. We proposed a new algorithm for solving such problem. We first implemented density-based sampling on spatial dataset to reduce data size, and then we partitioned the sample to different clusters in such a way, that each cluster forms a compact region in geometrical domain while has the similarity in non-geometrical domain. The experimental results show our algorithm is very effective and efficient.
期刊：
International Conference on Signal Processing Proceedings, ICSP,2007年1:343-346 ISSN：2164-5221
通讯作者：
Zhang, Meng
作者机构：
[Fu, Lihua; Li, Hongwei] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430074, Peoples R China.
通讯机构：
[Zhang, Meng] C;Cent China Normal Univ, Dept Comp Sci, Wuhan 430074, Peoples R China.
会议名称：
2006 8th International Conference on Signal Processing
会议时间：
2006-11-16
会议地点：
中国广西桂林
会议论文集名称：
Proceedings of 2006 8th International Conference on Signal Processing(Volume Ⅰof Ⅳ)
期刊：
Lecture Notes in Computer Science,2007年4491(PART 1):632-641 ISSN：0302-9743
通讯作者：
Zhang, Meng
作者机构：
[He, Tingting; Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Fu, Lihua] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Wang, Gaofeng] Wuhan Univ, CJ Huang Informat Technol Res Inst, Wuhan 430072, Peoples R China.
通讯机构：
[Zhang, Meng] C;Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
摘要：
Estimating the non-flat function which comprises both the steep variations and the smooth variations is a hard problem. The existing kernel methods with a single common variance for all the regressors can not achieve satisfying results. In this paper, a novel multi-scale model is constructed to tackle the problem by orthogonal least squares regression (OLSR) with wavelet kernel. The scheme tunes the dilation and translation of each wavelet kernel regressor by incrementally minimizing the training mean square error using a guided random search algorithm. In order to prevent the possible over-fitting, a practical method to select termination threshold is used. The experimental results show that, for non-flat function estimation problem, OLSR outperforms traditional methods in terms of precision and sparseness. And OLSR with wavelet kernel has a faster convergence rate as compared to that with conventional Gaussian kernel.
期刊：
International Conference on Signal Processing Proceedings, ICSP,2007年3:1943-+ ISSN：2164-5221
通讯作者：
Fu, Lihua
作者机构：
[Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Fu, Lihua] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
通讯机构：
[Fu, Lihua] C;China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
期刊：
SNPD 2007: EIGHTH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING, AND PARALLEL/DISTRIBUTED COMPUTING, VOL 3, PROCEEDINGS,2007年3:264-268
通讯作者：
Li, Hongwei
作者机构：
[Shen, Yuantong; Fu, Lihua; Li, Hongwei] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan, Peoples R China.
通讯机构：
[Li, Hongwei] C;China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
会议名称：
8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing/3rd ACIS International Workshop on Self-Assembling Wireless Networks
会议时间：
JUL 30-AUG 01, 2007
会议地点：
Qungdao, PEOPLES R CHINA
会议主办单位：
[Fu, Lihua;Li, Hongwei;Shen, Yuantong] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.^[Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan, Peoples R China.
关键词：
frequency estimation;group delay;time-varying cumulants;high resolution
摘要：
A novel approach for frequency estimation of one-dimensional harmonics in multiplicative and additive noises is presented. To alleviate the resolution limitation of the traditional cumulant-based algorithms, group delay (GD) function is utilized. In this new approach, GD is applied to the 2<sup>nd</sup> order time-varying cumulants of harmonics in complex noises, called as the 2<sup>nd</sup> order CGD. For a given frequency estimation problem, this paper derives a simple and practical algorithm. Numerical results show that the sharp peak and high resolution property of GD preserved, which outperform some traditional methods greatly.
期刊：
International Conference on Signal Processing Proceedings, ICSP,2007年1:272-275 ISSN：2164-5221
通讯作者：
Fu, Lihua
作者机构：
[Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Fu, Lihua; Li, Hongwei] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
通讯机构：
[Fu, Lihua] C;China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
会议名称：
2006 8th International Conference on Signal Processing
