Seasonal variation in water quality of Anchar Lake was evaluated using multivariate statistical techniques- principal component analysis (PCA) and cluster analysis (CA). Water quality data collected during 4 seasons was analyzed for 13 parameters. ANOVA showed significant variation in pH (F3 = 10.86, P < 0.05), temperature (F3 = 65, P

Seasonal variation in water quality of Anchar Lake was evaluated using multivariate statistical techniques- principal component analysis (PCA) and cluster analysis (CA). Water quality data collected during 4 seasons was analyzed for 13 parameters. ANOVA showed significant variation in pH (F3 = 10.86, P < 0.05), temperature (F3 = 65, P

Principal component analysis (PCA) for various types of image data is analyzed in terms of the forward and backward stepwise viewpoints. In the traditional forward view, PCA and approximating subspaces are constructed from lower dimension to higher dimension. The backward approach builds PCA in the reverse order from higher dimension to lower dimension. We see that for manifold data the backwar...

The widely adopted K-means clustering algorithm uses a sum of squared error objective function. A detailed analysis shows the close relationship between K-means clustering and principal component analysis (PCA) which is extensively utilized in unsupervised dimension reduction. We prove that the continuous solutions of the discrete K-means clustering membership indicators are the data projection...

Background and objectives: Hippophae rhamnoides L. known as sea buckthorn is a deciduous medicinal shrub belonging to Elaeagnaceae family. In this study, the most important chemical constituents of sea buckthornwere evaluated in wild populations of central Alborz Mountains in Iran during the growth season of 2014 and 2015. Methods: Phy...

Extra-virgin olive oil (EVOO) is among the basic constituents of the Mediterranean diet. Its nutraceutical properties are due mainly, but not only, to a plethora of molecules with antioxidant activity known as biophenols. In this article, several biophenols were measured in EVOOs from South Apulia, Italy. Hydroxytyrosol, tyrosol and their conjugated structures to elenolic acid in different form...

Principal Components Analysis (PCA) consists in nding the orthogonal directions of highest variance in a distribution of vectors. In this paper, we propose to extract the principal components of a random vector that partially results from a previous PCA. We demonstrate that this contextual PCA pro vides an optimal linear encoding of temporal con text. A recurrent neural netw ork based on this 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...

Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified...