This paper links the polarization-sensitive-array signal detection problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic PARAFAC signal detection algorithm. The proposed PARAFAC signal detection algorithm fully utilizes the polarization, spatial and temporal diversities, and supports s...

In our proposed system the random noise present in hyper spectral image is removed by means of tensor based decomposition methods. The noises present in hyper spectral images are classified into two categories namely: signal independent noise and signal dependent noise. The noises present in the hyper spectral images have dependence on the noise variance of the signal. The input image is separa...

Parallel factor (PARAFAC) analysis enables a quantitative analysis of excitation-emission matrix (EEM). The impact of a spectral variability stemmed from a diverse dataset on the representativeness of the PARAFAC model needs to be examined. In this study, samples from a river, effluent of a wastewater treatment plant, and algae secretion were collected and subjected to PARAFAC analysis. PARAFAC...

This 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 threeand higher way arrays. This link facilitates the derivation of powerful identifiability results for MI-SAP, shows that the uniqueness of singleand multiple-invariance ESP...

This paper explains the multi-way decomposition method PARAFAC and its use in chemometrics. PARAFAC is a generalization of PCA to higher order arrays, but some of the characteristics of the method are quite different from the ordinary two-way case. There is no rotation problem in PARAFAC, and e.g., pure spectra can be recovered from multi-way spectral data. One cannot as in PCA estimate compone...

This talk is an introduction to Independent Component Analysis (ICA) and Parallel Factor Analysis (PARAFAC), the way they are related and their links with Principal Component Analysis (PCA). PCA is now a standard technique for the analysis of two-way multivariate data, i.e., data available in matrix format. However, principal components are subject to rotational in-variance. By imposing statist...

This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic blind PARAFAC DS-CDMA receiver with performance close to nonblind minimum mean-squared error (MMSE). The propos...

A hybrid PARAFAC and principal-component model of tongue configuration in vowel production is presented, using a corpus of German vowels in multiple consonant contexts (fleshpoint data for seven speakers at two speech rates from electromagnetic articulography). The PARAFAC approach is attractive for explicitly separating speaker-independent and speaker-dependent effects within a parsimonious li...

High content of organic matter in the downstream of watersheds underscored the severity of non-point source (NPS) pollution. The major objectives of this study were to characterize and quantify dissolved organic matter (DOM) in watersheds affected by NPS pollution, and to apply self-organizing map (SOM) and parallel factor analysis (PARAFAC) to assess fluorescence properties as proxy indicators...