Partial Least Squares (PLS) is a wide class of methods for modeling relations between sets of observed variables by means of latent variables. It comprises of regression and classification tasks as well as dimension reduction techniques and modeling tools. The underlying assumption of all PLS methods is that the

A new method for classification is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by a support vector classifier. Unlike principal component analysis (PCA), which has previously served as a dimension reduction step for discrimination problems, orthonormalized PLS is closely related to Fisher’s approach to linear discrimination or equivalently to canonical correlation analysis. For this reason orthonormalized PLS is preferable to PCA for discrimination. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of classifying finger movement periods from non-movement periods based on electroencephalograms. 1.

In this paper we are presenting a novel multivariate analysis method. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. The algorithm is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method for feature extraction of labelled data.

This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data

Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data Sandra Waaijenborg and Aeilko H Zwinderman* Background: The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a classification that is likely to cause heterogeneity within the two groups. Rather than dividing the study population into cases and controls, it is better to identify the phenotype of a complex disease by a set of intermediate risk factors. But these risk factors often vary over time and are therefore repeatedly measured. Results: We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Via a two-step approach, we summarized the time courses of each individual and, secondly apply these to penalized nonlinear canonical correlation analysis to obtain sparse results. Conclusions: Application of this method to two datasets which study the genetic background of cardiovascular

PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. Visualising associations between paired `omics ' data sets

Abstract: In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be adjusted for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the Non-Metric NIPALS, the Non-Metric PLS Regression and the Non-Metric PLS Path Modeling, respectively. These algorithms extend the applicability of PLS methods to data measured on different measurement scales, as well as to variables linked by non-linear relationships.

Structure-activity models of oral clearance, cytotoxicity, and LD50:

Proceedings Penalized canonical correlation analysis to quantify the association between gene expression and DNA markers

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 renders a rich family of matching models when different regularization are enforced, with PLS as a special case. Particularly, with ℓ1 and ℓ2 type of regularization on the mapping functions, we obtain the model called Regularized Mapping to Latent Structures (RMLS). RMLS enjoys many advantages over PLS, including lower time complexity and easy parallelization. As another contribution, we give a generalization analysis of this matching framework, and apply it to both PLS and RMLS. In experiments, we compare the effectiveness and efficiency of RMLS and PLS on large scale web search problems. The results show that RMLS can achieve equally good performance as PLS for relevance ranking, while significantly speeding up the learning process. 1