Abstract: In this paper, we propose a novel algorithm to solve the row-sparse principal component analysis problem without relying on any data structure assumption. Sparse principal component analysis ...

What began as a typical prom in a town southwest of Oklahoma City became a surprise tribute when Principal Kirk Moore was crowned king weeks after heroically saving the lives of students by bravely ...

Part II: Unsupervised machine learning in R to cluster and identify candidate countries for international expansion, using PCA, K-Means, and DBSCAN.

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Electroencephalogram (EEG) analysis has shown significant research value for brain disease diagnosis, neuromodulation and brain-computer interface (BCI) application. The analysis and processing of EEG ...

ABSTRACT: This study applies Principal Component Analysis (PCA) to evaluate and understand academic performance among final-year Civil Engineering students at Mbeya University of Science and ...

The authors present a critique of current usage of principal component analysis in geometric morphometrics, making a compelling case with benchmark data that standard techniques perform poorly. The ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Mass spectrometry imaging (MSI) is constantly improving in spatial resolving power, ...

sispca is a Python package designed to learn linear representations capturing variations associated with factors of interest in high-dimensional data. It extends the Principal Component Analysis (PCA) ...