Robust principal component analysis for functional data

Locantore, Nicholas; Marron, J. S.; Simpson, Douglas G.; Zhang, J. T.; Boente, Graciela; Fraiman, Ricardo; Brumback, Babette; Croux, Christophe; Fan, Jianqing; Kneip, Aloïs; Marden, John I.; Peña, Daniel; Prieto, Javier; Ramsay, J. O.; Valderrama, Mariano J.; Aguilera, Ana M.; Tripoli, N.; Cohen, K. L.

doi:10.1007/bf02595862

Cited by 316 publications

(218 citation statements)

References 12 publications

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“…Later, Grenander (1950) Due to the theoretical and practical developments, FPCA has been successfully applied to many practical problems, such as the analysis of cornea curvature in the human eye (Locantore et al 1999), the analysis of electronic commerce , the analysis of growth curve (Chiou & Li 2007), the analysis of income density curves (Kneip & Utikal 2001), the analysis of implied volatility surface in finance (Cont & de Fonseca 2002), the analysis of longitudinal primary biliary liver cirrhosis (Yao et al 2005b), the analysis of spectroscopy data (Yao & Müller 2010), signal discrimination (Hall et al 2001), and time-course gene expression (Yao et al 2005a). Furthermore, Hyndman & Ullah (2007) proposed a smoothed and robust FPCA, and used it to forecast age-specific mortality and fertility rates.…”

Section: Functional Principal Component Analysis (Fpca)mentioning

confidence: 99%

A survey of functional principal component analysis

Shang

2013

AStA Adv Stat Anal

161

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Section: Functional Principal Component Analysis (Fpca)mentioning

confidence: 99%

A survey of functional principal component analysis

Shang

2013

AStA Adv Stat Anal

161

View full text Add to dashboard Cite

“…Substituting the ROBPCA by a simpler and faster robust PCA estimator such as spherical PCA [24] (sPCA) thus would be expected to decrease the computation time of the combined method. As most of the computation time is spent on the scatter identification procedure, we tried as well ROBPCA as a substitution with sPCA, in this step when looking at a set of real measurements (see below).…”

Section: Simulated Data Setmentioning

confidence: 99%

A fully robust PARAFAC method for analyzing fluorescence data

Engelen

Frosch

Jørgensen

2009

Journal of Chemometrics

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Parallel factor analysis (PARAFAC) is a widespread method for modeling fluorescence data by means of an alternating least squares procedure. Consequently, the PARAFAC estimates are highly influenced by outlying excitation-emission landscapes (EEM) and element-wise outliers, like for example Raman and Rayleigh scatter. Recently, a robust PARAFAC method that circumvents the harmful effects of outlying samples has been developed. For removing the scatter effects on the final PARAFAC model, different techniques exist. Newly, an automated scatter identification tool has been constructed. However, there still exists no robust method for handling fluorescence data encountering both outlying EEM landscapes and scatter. In this paper, we present an iterative algorithm where the robust PARAFAC method and the scatter identification tool are alternately performed. A fully automated robust PARAFAC method is obtained in that way. The method is assessed by means of simulations and a laboratory-made data set.

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“…A seed point is initialized for each foreground point and the points are successively merged into clusters based on neighboring distance. For each detected object, the technique of Principal Component Analysis (Locantore et al 1999) is used to identify the eigenvalues and eigenvectors corresponding to principal directions of the object geometry. Each object cluster is parameterized by a 4-dimensional feature vector encompassing the length (k 1 ), width (k 2 ), height (k 3 ), and elevation (k 4 ) of its bounding box.…”

Section: Object Recognition From Thermal-mapped Point Cloudmentioning

confidence: 99%

Robotic sensing and object recognition from thermal-mapped point clouds

Kim

Chen

Cho

2017

Int J Intell Robot Appl

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Many of the civil structures are more than half way through or nearing their intended service life; frequently assessing and maintaining structural integrity is a top maintenance priority. Robotic inspection technologies using ground and aerial robots with 3D scanning and imaging capabilities have the potential to improve safety and efficiency of infrastructure management. To provide more valuable information to inspectors and agency decision makers, automatic environment sensing and semantic information extraction are fundamental issues in this field. This paper introduces an innovative method for generating thermal-mapped point clouds of a robot's work environment and performing automatic object recognition with the aid of thermal data fused to 3D point clouds. The laser scanned point cloud and thermal data were collected using a custom-designed mobile robot. The multimodal data was combined with a data fusion process based on texture mapping. The automatic object recognition was performed by two processes: segmentation with thermal data and classification with scanned geometric features. The proposed method was validated with the scan data collected in an entire building floor. Experimental results show that the thermal integrated object recognition approach achieved better performance than a geometry only-based approach, with an average recognition accuracy of 93%, precision of 83%, and recall rate of 86% for objects in the tested environment including humans, display monitors and light fixtures.

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Robust principal component analysis for functional data

Abstract: cornea curvature maps, functional data, principal components analysis, robust statistics, spherical PCA, Zernike basis, 62H99,

Cited by 316 publications

References 12 publications

A survey of functional principal component analysis

A survey of functional principal component analysis

A fully robust PARAFAC method for analyzing fluorescence data

Robotic sensing and object recognition from thermal-mapped point clouds

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