The Independent Component Analysis (ICA) is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components—a stronger constraint that uses higher-order statistics—instead of the classical decorrelation (in the sense of ‘‘no correlation’’), which is a weaker constraint that uses only second-order statistics. This techniq...

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximizati...

Different multivariate statistical analysis such as, cluster analysis, principal component analysis, and multidimensional scale plot were employed to evaluate the trophic status of water quality for four monitoring stations. The present study was carried out to determine the physicochemical parameters of water and sediment characteristics of Pondicherry mangroves-southeast coast of India, durin...

Background Literature surrounding the statistical modeling of childhood growth data involves a diverse set of potential models from which investigators can choose. However, the lack of a comprehensive framework for comparing non-nested models leads to difficulty in assessing model performance. This paper proposes a framework for comparing non-nested growth models using novel metrics of predicti...

In fMRI both model-led and exploratory data-driven methods are used to identify groups of voxels according to their correlation either with an external reference or with some similarity measure. Here we present a technique to assess intragroup homogeneity using Kendall's coefficient of concordance W once groups have been identified. We show that the time-courses belonging to the group may be ra...

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in d-dimensional space R(d) and an integer k. The problem is to determine a set of k points in R(d), called centers, so as to minimize the mean squared dist...

To explore structural differences and similarities in multivariate multiblock data (e.g., a number of variables have been measured for different groups of subjects, where the data for each group constitute a different data block), researchers have a variety of multiblock component analysis and factor analysis strategies at their disposal. In this article, we focus on three types of multiblock c...

In this study, four statistical classifiers, namely linear discriminant classifier, quadratic discriminant classifier, k-Nearest Neighborhood classifier, and parzen classifier are considered for recognition of 2D-shapes. The octagonal shape features are identified from 2D-shapes with the morphological shape decomposition technique. These features are reduced using principle component analysis. ...

Identification of clay minerals based on chemometric analysis of measured infrared (IR) spectra was suggested. IR spectra were collected using the diffuse reflection technique. Discriminant analysis and principal component analysis were used as chemometric methods. Four statistical models were created for separation and identification of clay minerals. More than 50 samples of various clay miner...

In this article, we investigate clustering methods for multilevel functional data, which consist of repeated random functions observed for a large number of units (e.g., genes) at multiple subunits (e.g., bacteria types). To describe the within- and between variability induced by the hierarchical structure in the data, we take a multilevel functional principal component analysis (MFPCA) approac...