Results 241 to 250 of about 544,201 (272)

A constrained least square and trimmed least square method for multisensor data fusion

International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003, 2003
Though neural data fusion algorithms based on a linearly constrained least square (LCLS) method solve the ill-conditioned and singular matrix problems that arise in the LCLS method, they don't perform well when there are impulsive noises attached to several sensors.
null Haiyan Shi, null Zhongliang Jing, H. Leung   +2 more
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Computing least trimmed squares regression with the forward search

Statistics and Computing, 1999
Least trimmed squares (LTS) provides a parametric family of high breakdown estimators in regression with better asymptotic properties than least median of squares (LMS) estimators. We adapt the forward search algorithm of Atkinson (1994) to LTS and provide methods for determining the amount of data to be trimmed.
Atkinson A.C;鄭宗記, Cheng,Tsung-Chi
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Robust Sensor Bias Estimation Based on the Bounded Variables Least Trimmed Squares

Chinese Control and Decision Conference, 2019
Sensor bias estimation is of significant importance to ensure the estimation accuracy of target state for the distributed multi-radar data fusion system.
Wei Tian, Gaoming Huang, Huafu Peng, Haibin Wang, Xiaohong Lin, Xuebao Wang   +5 more
semanticscholar   +1 more source

A Genetic Algorithm Implementation of the Fuzzy Least Trimmed Squares Clustering

2007 IEEE International Fuzzy Systems Conference, 2007
This paper describes a new approach to finding a global solution for the fuzzy least trimmed squares clustering. The least trimmed squares (LTS) estimator is known to be a high breakdown estimator, in both regression and clustering. From the point of view of implementation, the feasible solution algorithm is one of the few known techniques that ...
Amit Banerjee, Sushil J. Louis
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An Exact Least Trimmed Squares Algorithm for a Range of Coverage Values

Journal of Computational and Graphical Statistics, 2010
A new algorithm to solve exact least trimmed squares (LTS) regression is presented. The adding row algorithm (ARA) extends existing methods that compute the LTS estimator for a given coverage. It employs a tree-based strategy to compute a set of LTS regressors for a range of coverage values. Thus, prior knowledge of the optimal coverage is not required.
Hofmann, Marc H., Gatu, Cristian, Kontoghiorghes, Erricos John   +2 more
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Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization

2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010
Collaborative filtering (CF) recommender systems help people discover what they really need in a large set of alternatives by analyzing the preferences of other related users. Recent research has shown that the accuracy of recommendations can be improved significantly by using matrix factorization (MF) models.
Zunping Cheng, Neil Hurley
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A note on the breakdown point of the least median of squares and least trimmed squares estimators

Statistics & Probability Letters, 1993
A notion of d-fullness is introduced to study a robust extension of the maximum likelihood principle. Some results about the breakdown point of existing robust estimators follow.
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Adaptive least trimmed squares fuzzy neural network

2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012), 2012
In this paper, we propose the adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies the scale estimate to the least trimmed squares fuzzy neural network (LTS-FNN). The emphasis of this paper is particular on the robustness against the outliers and the choice of the trimming constant can be determined adaptively.
Jyh-Yeong Chang, Shih-Hui Liao, Chin-Teng Lin   +2 more
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Least trimmed squares based CPBUM neural networks

Proceedings 2011 International Conference on System Science and Engineering, 2011
In this paper, least trimmed squares (LTS) based CPBUM neural networks are proposed to improve the outliers and noise problems of conventional neural networks. In general, the obtained training data in the real applications maybe contain the outliers and noise.
Jin-Tsong Jeng, Chi-Ta Chuang, Chen-Chia Chuang   +2 more
openaire   +1 more source

The BAB algorithm for computing the total least trimmed squares estimator

Journal of Geodesy, 2020
Zhipeng Lv, Lifen Sui
semanticscholar   +1 more source