Projection to Latent Structures (PLS), also known as Partial Least Squares, is a method for matching objects from two heterogeneous domains. Although PLS is empirically verified effective for matching queries and documents, its scalability becomes a major hurdle for its application in real-world web search. In this paper, we study a general framework for matching heterogeneous objects, which re...

Three men were initially diagnosed as having primary lateral sclerosis (PLS), but eventually developed amyotrophic lateral sclerosis (ALS) after 7.5, 9, and at least 27 years. Non-familial ALS and PLS might be different manifestations of a single disease or constitute completely distinct entities. The clinical diagnosis of PLS predicts a median survival that is four to five times longer than in...

We propose a novel framework that combines penalization techniques with Partial Least Squares (PLS). We focus on two important applications. (1) We combine PLS with a roughness penalty to estimate high-dimensional regression problems with functional predictors and scalar response. (2) Starting with an additive model, we expand each variable in terms of a generous number of B-Spline basis functi...

This paper adds to an important aspect of Partial Least Squares (PLS) path modeling, namely the convergence of the iterative PLS path modeling algorithm. Whilst conventional wisdom says that PLS always converges in practice, there is no formal proof for path models with more than two blocks of manifest variables. This paper presents six cases of non-convergence of the PLS path modeling algorith...

Composite-based methods like partial least squares (PLS) path modeling have an advantage over factor-based methods (like CB-SEM) because they yield determinate predictions, while factor-based methods’ prediction is constrained in this regard by factor indeterminacy. To maximize practical relevance, research findings should extend beyond the study’s own data. We explain how PLS practices, derivi...

This paper resumes the discussion in information systems research on the use of partial least squares (PLS) path modeling and shows that the inconsistency of PLS path coefficient estimates in the case of reflective measurement can have adverse consequences for hypothesis testing. To remedy this, the study introduces a vital extension of PLS: consistent PLS (PLSc). PLSc provides a correction for...

Some of the models using partial least squares (PLS) in Information Systems (IS) field may have serious problems because do not properly address endogeneity. This may suppose a problem in IS theory building because it may lead IS scholars to non-correct results. Although the IS community’s awareness is rising, we do not have a clear understanding of the problem nor fine-grained practical guidel...

Partial Least Squares (PLS) is a ubiquitous statistical technique for bilinear factor analysis. It is used in many data analysis, machine learning, and information retrieval applications to model the covariance structure between a pair of data matrices. In this paper, we consider PLS for representation learning in a multiview setting where we have more than one view in data at training time. Fu...

Abstract Partial least squares structural equation modeling (PLS-SEM) is a modern multivariate analysis technique with demonstrated ability to estimate theoretically established cause-effect relationship models. This has been increasingly adopted in construction management research over the last two decades. Accordingly, critical review of studies adopting PLS-SEM appears be timely and valuable...

As cloud computing has become a mature technology broadly being adopted by companies across all industries, cloud service providers are increasingly turning their attention to retaining their customers. However, only little research has been conducted on investigating the antecedents of service continuance in an organizational context. To address this gap in research, we carried out a quantitat...