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 ...

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 ...

With the increasing complexity of analytical data nowadays, great reliance on statistical and chemometric software is quite common for scientists. Powerful open-source software, such as Python, R, 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 ...

Sentiment analysis, i.e., determining the emotional tone of a text, has become a crucial tool for researchers, developers, and businesses to comprehend social media trends, consumer feedback, and ...

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, ...

Network meta-analysis is a statistical method that allows for comparing three or more interventions in a single framework, by synthesizing direct and indirect evidence from multiple studies which ...

PCA is an important tool for dimensionality reduction in data science and to compute grasp poses for robotic manipulation from point cloud data. PCA can also directly used within a larger machine ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on a classical ML technique that transforms a dataset into one with fewer columns, useful for creating a graph of ...

Abstract: Efficient representations of data are essential for processing, exploration, and human understanding, and Principal Component Analysis (PCA) is one of the most common dimensionality ...