the aim of this study was to assess the environmental impact of socio-cultural practices on the water quality of river ganga at the foothills of the garhwal himalayas in uttarakhand state, india. the physico-chemical parameters that contributed to the temporal variation and pollution in the river were identified in this study. principal component analysis (pca) and cluster analysis (ca) were us...

Principal Component Analysis (PCA) is a frequently used tool to analyse, visualize and reduce the dimensionality of data occurring in a variety of fields in science and engineering. Given a data matrix X ∈ Rn×p (where n is the number of points and p is the dimensionality), PCA finds a set of d(≪ p) orthonormal vectors V = {v1, v2, . . . , vd} in R such that the span(V ) explains the maximum amo...

Principal component analysis (PCA) is possibly one of the most widely used statistical tools to recover a low-rank structure of the data. In the highdimensional settings, the leading eigenvector of the sample covariance can be nearly orthogonal to the true eigenvector. A sparse structure is then commonly assumed along with a low rank structure. Recently, minimax estimation rates of sparse PCA w...

This paper presents a comparative analysis of a new unsupervised PCA-based technique for steel plates texture segmentation towards defect detection. The proposed scheme called Variance Based Component Analysis or VBCA employs PCA for feature extraction, applies a feature reduction algorithm based on variance of eigenpictures and classifies the pixels as defective and normal. While the classic P...

In this paper the performance of oversampling methods such as SMOTE (Synthetic Minority Over-sampling Technique) and PCA (Principal Component Analysis) which are used for preprocessing are applied for the Brain computer interface dataset. The pre-processed data is used for classification by SMO and Naïve Bayes. In the EEG recordings, the transient events are detected while predicting the condit...

background: cutaneous leishmaniasis is one of the most important parasitic diseases in humans. in this disease, one of the responsible organisms is leishmania major, which is transmitted by sandfly vector. there are specific differences in biochemical profiles and metabolite pathways in logarithmic and stationary phases of leishmania parasites. in the present study, 1h nmr spectroscopy was used...

eight selected heavy metals (cu, zn, pb, cd, cr, mn, fe and al) in surface and sub-surfacesediments in the northern coast of sfax (tunisia) were studied in order to assess the sediment quality and tohighlight the anthropogenic contributions to heavy metal distributions in the two study sediment levels.multiple chemometric approaches based on geographic information system (gis), enrichment facto...

A clustering is process to identify a homogeneous groups of object called as cluster. Clustering is one interesting topic on data mining. A group or class behaves similarly characteristics. This paper discusses a robust clustering process for data images with two reduction dimension approaches; i.e. the two dimensional principal component analysis (2DPCA) and principal component analysis (PCA)....

This paper proposes a novel approach using two-dimensional principal component analysis (2D-PCA) and local direction descriptor for face recognition. The proposed method utilizes the transformed image obtained from local direction descriptor as the direct input image of 2D-PCA algorithms. The performance comparison was performed using principal component analysis (PCA) and Gabor-wavelets based ...

We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the l2 norm with a ...