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Study on least trimmed squares fuzzy neural networks
2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, 2010In this paper, least trimmed squares (LTS) estimators, frequently used in robust (or resistant) linear parametric regression problems, will be generalized to nonparametric LTS-fuzzy neural networks (LTS-FNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers.
Hsu-Kun Wu, Jer-Guang Hsieh, Ker-Wei Yu
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Least Trimmed Squares for Regression Models with Stable Errors
Fluctuation and Noise Letters, 2023Least Trimmed Squares (LTS) is a robust regression method with respect to outliers. It is based on performing Ordinary Least Squares (OLS) estimates on sub-datasets and determining the optimal solution corresponding to the minimum sum of squared residuals. Since the method of LTS is based on OLS, errors in regression models have finite variance.
Mohammad Bassam Shiekh Albasatneh +1 more
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Least Tail-Trimmed Squares for Infinite Variance Autoregressions
SSRN Electronic Journal, 2012We develop a robust least squares estimator for autoregressions with possibly heavy tailed errors. Robustness to heavy tails is ensured by negligibly trimming the squared error according to extreme values of the error and regressors. Tail‐trimming ensures asymptotic normality and super‐‐convergence with a rate comparable to the highest achieved amongst
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Trimmed diffusion least mean squares for distributed estimation
2015 IEEE International Conference on Digital Signal Processing (DSP), 2015We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate network parameters from noisy measurements. The problem is important when modeling a wide class of real-time sensor networks, where efficiency, robustness, and low power consumption are desired features. In this work, we focus on diffusion-based
Hong Ji, Xiaohan Yang, Badong Chen
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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, 2003Though 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 +2 more
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Sparse Principal Component Analysis Based on Least Trimmed Squares
Technometrics, 2019Sparse principal component analysis (PCA) is used to obtain stable and interpretable principal components (PCs) from high-dimensional data.
Yixin Wang, Stefan Van Aelst
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Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization
2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010Collaborative 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 Genetic Algorithm Implementation of the Fuzzy Least Trimmed Squares Clustering
2007 IEEE International Fuzzy Systems Conference, 2007This 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, 2010A 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. +2 more
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2018
We look at different approaches to learning the weights of the weighted arithmetic mean such that the median residual or sum of the smallest half of squared residuals is minimized. The more general problem of multivariate regression has been well studied in statistical literature, however in the case of aggregation functions we have the restriction on ...
Gleb Beliakov +2 more
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We look at different approaches to learning the weights of the weighted arithmetic mean such that the median residual or sum of the smallest half of squared residuals is minimized. The more general problem of multivariate regression has been well studied in statistical literature, however in the case of aggregation functions we have the restriction on ...
Gleb Beliakov +2 more
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