Results 221 to 230 of about 846,956 (260)
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Parallel factor analysis in sensor array processing
IEEE Transactions on Signal Processing, 2000This paper links multiple invariance sensor array processing (MI-SAP) to parallel factor (PARAFAC) analysis, which is a tool rooted in psychometrics and chemometrics. PARAFAC is a common name for low-rank decomposition of three- and higher way arrays.
Sidiropoulos, N. D. +2 more
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On rotational ambiguity in parallel factor analysis
Chemometrics and Intelligent Laboratory Systems, 2010Abstract Although, in many cases parallel factor analysis (PARAFAC) resolves the trilinear data arrays to the true physical factors that form the data, i.e., unique solution can be found, the algorithm does not always converge to chemically meaningful solutions. Kiers and Smilde [J. Chemom.
H. Abdollahi, S.M. Sajjadi
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Comprehensive Three-Dimensional Gas Chromatography with Parallel Factor Analysis
Analytical Chemistry, 2007Development of a comprehensive, three-dimensional gas chromatograph (GC3) instrument is described. The instrument utilizes two six-port diaphragm valves as the interfaces between three, in-series capillary columns housed in a standard Agilent 6890 gas chromatograph fitted with a high data acquisition rate flame ionization detector.
Nathanial E, Watson +3 more
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Detecting outlying samples in a parallel factor analysis model
Analytica Chimica Acta, 2011To explore multi-way data, different methods have been proposed. Here, we study the popular PARAFAC (Parallel factor analysis) model, which expresses multi-way data in a more compact way, without ignoring the underlying complex structure. To estimate the score and loading matrices, an alternating least squares procedure is typically used. It is however
Sanne, Engelen, Mia, Hubert
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Parallel Factor Analysis for Gas Sensor Array Signals
Applied Mechanics and Materials, 2014Data from sensor array are often arranged in three-dimension as sample × time × sensor. Traditional methods are mainly used for two-dimension data. When such methods are applied, some time-profile information will lost. To acquire the information of samples, sensors and times more exactly, parallel factor analysis (PARAFAC) is investigated to deal with
Wen Na Zhang, Guo Jun Qin, Niao Qing Hu
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Parallel factor analysis of ovarian autofluorescence as a cancer diagnostic
Lasers in Surgery and Medicine, 2012AbstractBackground and ObjectivesEndogenous fluorescence from certain amino acids, structural proteins, and enzymatic co‐factors in tissue is altered by carcinogenesis. We evaluate the potential of these changes in fluorescence to predict a diagnosis of malignancy and to estimate the risk of developing ovarian cancer.Study Design/Materials and ...
Ronie, George +3 more
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Spectrofluorimetric determination of chlorophylls and pheopigments using parallel factor analysis
Talanta, 2001In this study, parallel factor analysis (PARAFAC) was applied to fluorescence excitation emission matrices (EEM) of chlorophylls and pheopigments dissolved in acetone:water (9:1). The excitation wavelength range was from 350 to 500 nm and the emission was recorded from 600 to 730 nm.
L, Moberg, G, Robertsson, B, Karlberg
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On uniqueness and selectivity in three-component parallel factor analysis
Analytica Chimica Acta, 2013Unambiguous recovery of profiles is a distinguishable advantage of Parallel Factor Analysis (PARAFAC) as a trilinear model and has made it a promising exploratory tool for data analysis. Linear dependency in profiles destroys trilinearity and will increase ambiguity in the curve resolution of three-way data sets. PARAFAC uniqueness deteriorates totally
Nematollah, Omidikia +2 more
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Evaluation of variation matrix arrays by parallel factor analysis
Journal of Chemometrics, 2008AbstractPARAFAC is one of the most widely used algorithms for trilinear decomposition. The uniqueness properties of the PARAFAC model are very attractive regardless of whether one is interested curve resolution or not. The fact that PARAFAC provides one unique solution simplifies interpretation of the model.
Hamid Abdollahi, S. Maryam Sajjadi
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Spatially constrained parallel factor analysis for semi-blind beamforming
2011 Seventh International Conference on Natural Computation, 2011Parallel factor analysis (PARAFAC) has found numerous applications in blind signal processing, mainly due to its nice identifiability. However, the standard PARAFAC decomposition does not use prior information on the mixing procedure, which could actually be roughly estimated.
Xiao-Feng Gong, Qiu-Hua Lin
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