Two new methods to select groups of variables have been developed for multiblock data: ”Group Sparse Principal Component Analysis” (GSPCA) for continuous variables and ”Sparse Multiple Correspondence Analysis” (SMCA) for categorical variables. GSPCA is a compromise between Sparse PCA method of Zou, Hastie and Tibshirani and the method ”group Lasso” of Yuan and Lin. PCA is formulated as a regres...

The elements of a multivariate data set are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has complete measurements for each individual curve or, as is more common, one has measurements on a fine grid taken at the same time points for all curves, then many standard techniques may be applied. However,...

Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we show that PCA and PCO can be carried out under regression frameworks. Thus, it is convenient to incorporate sparse techniques into the regression frameworks. In particular, we propose a sparse PCA model and a sparse PCO model. The for...

herbal water is referred to the liquid obtained from the distillation of medicinal plants. different parts of plants, such as flowers, fruits, leaves, seeds and roots have long been used to produce herbal waters. herbal waters are used as dietary supplements and alternative medicine and are commonly used for flavoring in baking. previous studies focused on the non-volatile constituents of herbs...

In this paper we proposed an iterative elimination algorithm for sparse principal component analysis. It recursively eliminates variables according to certain criterion that aims to minimize the loss of explained variance, and reconsiders the sparse principal component analysis problem until the desired sparsity is achieved. Two criteria, the approximated minimal variance loss (AMVL) criterion ...

The effect of nonstationarity in time series columns of input data in principal components analysis is examined. This usually happens when indexing economic indicators for monitoring purposes. The first component averages all the variables without reducing dimensionality. As an alternative, sparse principal components analysis can be used but attainment of sparsity among the loadings is influen...