SUMMARYThe alternating least squares PARAFAC algorithm is a useful tool for resolving trilinear three-way data arrays. Occasionally, however, it becomes bogged down for many iterations in the vicinity of a poor quality resolution before moving on to a much superior optimum fit. We investigate this behavior in a simulation study and suggest ways of overcoming the obstacles it presents.
Field screening of fuel-contaminated soils using laser-induced fluorescence is a cost effective and timely method of characterizing contaminated sites. Data collected with laser-based screening tools are often extensive and difficult to interpret. Pattern recognition algorithms can be utilized to enable less highly trained personnel to identify contaminants. In this work, fluorescence intensity of various hydrocarbon fuels deposited on various soil types was measured as a function of emission wavelength and decay time, generating wavelength-time matrices (WTMs). The data were arranged into a three mode array and subjected to trilinear decomposition (TLD). The results ofthe TLD were then utilized in pattern recognition schemes, specifically, linear discrimination and classification and hierarchical cluster analysis. Classification rates and clustering results indicate that these techniques can be very valuable tools in site characterization.
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