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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. These methods typically map image features ( X) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing discriminative methods is that
Dong Huang 0007 +2 more
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Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. These methods typically map image features ( X) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing discriminative methods is that
Dong Huang 0007 +2 more
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2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017
In this paper, we consider the use of deep neural networks in the context of robust regression. We address the standard linear model with observations that are corrupted by outliers. We build upon Huber's robust regression and the classical least trimmed squares estimator, and propose a deep neural network that generalizes both and provides high ...
Diskin, Tzvi +3 more
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In this paper, we consider the use of deep neural networks in the context of robust regression. We address the standard linear model with observations that are corrupted by outliers. We build upon Huber's robust regression and the classical least trimmed squares estimator, and propose a deep neural network that generalizes both and provides high ...
Diskin, Tzvi +3 more
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Journal of Statistical Planning and Inference, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Adrover, Jorge +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Adrover, Jorge +2 more
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Robust Regression for Face Recognition
2010 20th International Conference on Pattern Recognition, 2010In this paper we address the problem of robust face recognition by formulating the pattern recognition task as a problem of robust estimation. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace (Barsi and Jacobs, 2003 [1]), we develop a linear model representing a probe image as a linear ...
Imran Naseem +2 more
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2010
Within disaggregation–aggregation approach, ordinal regression aims at inducing parameters of a preference model, for example, parameters of a value function, which represent some holistic preference comparisons of alternatives given by the Decision Maker (DM).
Greco, Salvatore +3 more
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Within disaggregation–aggregation approach, ordinal regression aims at inducing parameters of a preference model, for example, parameters of a value function, which represent some holistic preference comparisons of alternatives given by the Decision Maker (DM).
Greco, Salvatore +3 more
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Robust regression: An introduction
Analytical Methods, 2012Analytical scientists use regression methods in two main areas. Calibration graphs are used with the results of instrumental analyses to obtain concentrations from test samples. Graphical methods are used to evaluate the results obtained when two methods, often a novel one and a reference one, are compared by applying them to the same set of test ...
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2022
This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.
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This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.
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A robust Liu regression estimator
Communications in Statistics - Simulation and Computation, 2017The least-squares regression estimator can be very sensitive in the presence of multicollinearity and outliers in the data. We introduce a new robust estimator based on the MM estimator.
Peter Filzmoser, Fatma Sevinç Kurnaz
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Robust Principal Components Regression
2002We consider the multivariate linear regression model with p explanatory variables X and q ≥ 1 response variables Y. Moreover we assume that the regressors are multicollinear. This situation often occurs in the calibration of chemometrical data, where the X-variables correspond with spectra that are measured at many frequencies.
Verboven, Sabine, Hubert, Mia
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Robust Sets of Regression Estimates
Econometrica, 1983Gilstein, C Zachary, Leamer, Edward E
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