Proximity to an adaptive peak influences a lineage's potential to diversify. We tested whether piscivory, a high quality but functionally demanding trophic strategy, represents an adaptive peak that limits morphological diversification in the teleost fish clade, Centrarchidae. We synthesized published diet data and applied a well-resolved, multilocus and time-calibrated phylogeny to reconstruct...

This work proposes a new methodology for sensor fault detection and localization using principal component analysis (PCA). A new index is proposed in order to detect simple and multiple faults affecting the dependent and independent process variables. A new iterative selection method of principal component number is presented. This method determines a model allowing the detection of faults with...

the writers of this research have studied seven variables including: precipitation, relative humidity, sunny hours, temperature average, temperature minimum, temperature maximum and sea level pressure, at sanandaj air station during 1964-1994. sanandaj air station has nearly 10966 days of complete data for these variables. a principal component analysis (pca) has been applied on this data; then...

Hyperspectral reflectance of normal and lodged rice caused by rice brown planthopper and rice panicle blast was measured at the canopy level. Over one decade broadand narrow-band vegetation indices (VIs) were calculated to simulate Landsat ETM+ with in situ hyperspectral reflectance. Principal component analysis (PCA) was utilized to obtain the front two principal components (PCs). Probabilisti...

the principle of dimensionality reduction with pca is the representation of the dataset ‘x’in terms of eigenvectors ei ∈ rn  of its covariance matrix. the eigenvectors oriented in the direction with the maximum variance of x in rn carry the most      relevant information of x. these eigenvectors are called principal components [8]. assume that n images in a set are originally represented in mat...

In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...

In this paper we compare and contrast the objectives of principal component analysis and exploratory factor analysis. This is done through consideration of nine examples. Basic theory is presented in appendices. As well as covering the standard material, we also describe a number of recent developments. As an alternative to factor analysis, it is pointed out that in some cases it may be useful ...

Face recognition is emerging as an active research area with numerous commercial and law enforcement applications. This paper presents comparative analysis of two most popular subspace projection techniques for face recognition. It compares Principal Component Analysis (PCA) and Independent Component Analysis (ICA), as implemented by the InfoMax algorithm. ORL face database is used for training...