Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...

Abstract: Principal Component Analysis (PCA) is one of the most important unsupervised dimensionality reduction algorithms, which uses squared $\ell _{2}$ -norm to make it very sensitive to outliers.

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...

ABSTRACT: With the rapid development of science and technology, artificial intelligence plays a significant role across various domains. In recent years, frequent occurrences of natural disasters and ...

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the ...

DUBLIN--(BUSINESS WIRE)--The "Bakery Processing Equipment Market - Equipment Types, Applications and End-Use Sectors" report has been added to ResearchAndMarkets.com's offering. Worldwide, the demand ...

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 ...

Gravitational wave detection is one of the most cutting-edge research areas in modern physics, with its success relying on advanced data analysis and signal processing techniques. This study provides ...