Multilinear regression - Open Access .click

IEEE Transactions on Systems, Man, and Cybernetics, 2012
Conventional regression methods, such as multivariate linear regression (MLR) and its extension principal component regression (PCR), deal well with the situations that the data are of the form of low-dimensional vector. When the dimension grows higher, it leads to the under sample problem (USP): the dimensionality of the feature space is much higher ...
Ya Su, Xinbo Gao, Xuelong Li
exaly +4 more sources

Regression coefficients in multilinear PLS

Journal of Chemometrics, 1998
Three alternative approaches are discussed for finding the final calibration model (regression coefficients) in PLS regression of k-way Y on N-way X. The simplest approach is to skip the deflation of the X-data. From the observation that the specific deflation used in multiway PLS is inconsequential, it also follows that Bro's tri-PLS is equivalent to ...
S de Jong
exaly +2 more sources

Prediction of Sweetness by Multilinear Regression Analysis and Support Vector Machine

Journal of Food Science, 2013
Abstract The sweetness of a compound is of large interest for the food additive industry. In this work, 2 quantitative models were built to predict the logSw (the logarithm of sweetness) of 320 unique compounds with a molecular weight from 132 to 1287 and a sweetness from 22 to 22500000.
Aixia Yan, Qipeng Yuan
exaly +3 more sources

fos: computer and information sciences
regression analysis
humans

computer science - machine learning
machine learning cs.lg
6. clean water

linear regression
regression
machine learning