Abstract: We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to ...

Data analysis and statistics are essential skills for anyone who wants to work with data and extract meaningful insights from it. Whether you are a researcher, a business analyst, a data scientist, or ...

Implemented PCA algorithm from scratch on MNIST Dataset. Visualizing the reconstructed images made and comparing them with the original image. Visualizing the ...

Abstract: Principal Component Analysis (PCA) is a widely used technique in process monitoring, fault diagnosis, and soft sensing of industrial systems. Despite its popularity, PCA suffers from the ...

Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks ...

Track geometry data is often combined into a single parameter index referred to as a Track Quality Index or TQI. TQIs exhibit classical big data attributes: value, volume, velocity, veracity and ...

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of ...

ABSTRACT: The goal of this study is to verify the relationship between adult children’s perception about their parents’ conjugality and these children’s conjugal skills today, as married people.