Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. While PCA often thought as dimensionality reduction method, purpose actually two-fold: dimension and uncorrelated feature Furthermore, enormity dimensions sample size modern day datasets have rendered centralized solutions unusable. In that vein, this paper reconsiders problem when samp...

Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionali...

In recent years, principal component analysis (PCA) has attracted great attention in image compression. However, since the compressed image data include both the transformation matrix (the eigenvectors) and the transformed coefficients, PCA cannot produce the same performance as DCT (discrete Cosine transform) in respect of compression ratio. In using DCT, we need only to preserve the coefficie...

BACKGROUND Single Nucleotide Polymorphisms (SNPs) are one of the largest sources of new data in biology. In most papers, SNPs between individuals are visualized with Principal Component Analysis (PCA), an older method for this purpose. PRINCIPAL FINDINGS We compare PCA, an aging method for this purpose, with a newer method, t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualiza...

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

The paper describes an approach to dynamic multivariate analysis in which principal component analysis (PCA) is combined with integral transform techniques. The aim was to detect correlations when process dynamics cause lags or time delays. The techniques give a signature that characterises the correlated measurements. Tools for Fourier and wavelet PCA have been developed and tested. They have ...

In order to extract any ULF signature associated with earthquakes, the principal component analysis (PCA) and singular spectral analysis (SSA) have been performed to investigate the possibility of discrimination of signals from different sources (geomagnetic variation, artificial noise, and the other sources (earthquake-related ULF emissions)). We adopt PCA to the time series data observed at c...