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Subspace Segmentation by Correlation Adaptive Regression
IEEE Transactions on Circuits and Systems for Video Technology, 2018Subspace segmentation aims to segment a given data set into clusters with each cluster corresponding to a subspace. Most recent works focus on subspace representation-based methods, which construct the affinity matrix based on the subspace representation of the data points.
Weiwei Wang 0005 +2 more
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Segmented relationships to model erosion of regression effect in Cox regression
Statistical Methods in Medical Research, 2010In this article we propose a parsimonious parameterisation to model the so-called erosion of the covariate effect in the Cox model, namely a covariate effect approaching to zero as the follow-up time increases. The proposed parameterisation is based on the segmented relationship where proper constraints are set to accomodate for the erosion.
MUGGEO, Vito Michele Rosario +1 more
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Burr regression and portfolio segmentation
Insurance: Mathematics and Economics, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Beirlant, Jan +3 more
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Segmented Linear Regression Trees
Acta Mathematica Sinica, English SerieszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zheng, Xiangyu, Chen, Songxi
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Applications of segmented regression models for biomedical studies
American Journal of Physiology-Endocrinology and Metabolism, 1996In many biological models, a relationship between variables may be modeled as a linear or polynomial function that changes abruptly when an independent variable obtains a threshold level. Usually, the transition point is unknown, and a major objective of the analysis is its estimation. This type of model is known as a segmented regression model.
N G, Berman +3 more
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Random changepoint segmented regression with smooth transition
Statistical Methods in Medical Research, 2020We consider random changepoint segmented regression models to analyse data from a study conducted to verify whether treatment with stem cells may delay the onset of a symptom of amyotrophic lateral sclerosis in genetically modified mice. The proposed models capture the biological aspects of the data, accommodating a smooth transition between the ...
Julio M Singer +5 more
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Segmented regression for spatio-spectral background estimation
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017We formulate hyperspectral target detection in terms of a local context by modeling the relationship of individual pixels with the annuli of pixels that surround them. A prediction of the center pixel in terms of the annulus pixels provides an estimate of the target-free pixel value, and this estimate can be used as a baseline against which a ...
James Theiler, Amanda Ziemann
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Multiple regression estimation for motion analysis and segmentation
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005This paper describes multiple model estimation for motion analysis and segmentation (aka spatial partitioning), from point correspondences in two successive images. In motion analysis applications, available (training) data is generated by several unknown models (motions).
Vladimir Cherkassky +2 more
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DBRS2: dense boundary regression for semantic segmentation
Journal of Electronic Imaging, 2018Most of the current semantic segmentation approaches have achieved state-of-the-art performance relying on fully convolutional networks. However, the consecutive operations such as pooling or convolution striding lead to spatially disjointed object boundaries.
Jinfu Yang +3 more
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A fuzzy clusterwise regression approach to benefit segmentation
International Journal of Research in Marketing, 1989Abstract An algorithm for fuzzy clusterwise regression (FCR) is proposed which can be used for benefit segmentation within the framework of preference analysis. The method simultaneously estimates the models relating preference to product dimensions within each cluster, as well as the parameters indicating the degree of membership of subjects in ...
Wedel, M., Steenkamp, J.E.B.M.
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