was related to a procedure called the three-pass regression filter (3PRF). Supposing the number of observations and variables are large, the 3PRF (and hence PLS) is asymptotically normal for the "best" forecast implied by a linear latent factor model. In stock market data, PLS has been shown to provide accurate out-of-sample forecasts of returns and cash-flow growth. Partial Least Square SVD A PLS version based on singular value decomposition (SVD) provides a memory efficient implementation that can be used to address high-dimensional problems, such as relating millions of genetic markers to thousands of imaging features in imaging genetics, on consumer-grade hardware. PLS correlation PLS correlation (PLSC) is another methodology related to PLS regression, which has been used in neuroimaging and sport science, to quantify the strength of the relationship between data sets. Typically, PLSC divides the data into two blocks (sub-groups) each containing one or more variables, and then uses singular value decomposition (SVD) to establish the strength of any relationship (i.e. the amount of shared information) that might exist between the two component sub-groups. It does this by using SVD to determine the inertia (i.e. the sum of the singular values) of the covariance matrix of the sub-groups under consideration. See also Canonical correlation Data mining Deming regression Feature extraction Machine learning Partial least squares path modeling Principal component analysis Regression analysis Total sum of squares Literature Weblinks A short introduction to PLS regression and its history Video: Derivation of PLS by Prof. H. Harry Asada References Latent variable models Least squares Articles with example pseudocode