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 ...

An artist’s impression of a quantum electrodynamics simulation using 100 qubits of an IBM quantum computer. The spheres and lines denote the qubits and connectivity of the IBM quantum processor; gold ...

Abstract: This paper presents a PCA (Principal Component Analysis) data dimensionality reduction algorithm based on OPNs (Ordered Pair of Normalized Real Numbers), referred to as OPNs-PCA. This ...

LinkedIn support accidentally revealed its algorithm: it tracks "viewer tolerance," reducing visibility for authors whose posts are consistently ignored. To succeed, diversify content types weekly, ...

Learn the Adagrad optimization algorithm, how it works, and how to implement it from scratch in Python for machine learning models. #Adagrad #Optimization #Python Trump administration looking to sell ...

Algorithms, which are just sets of instructions expressed in code, are harder to restrict than physical goods. But governments, including the U.S., have long tried to prevent their export. The ...

PCA total explained variance ratio is ALWAYS equal to ONE for any number of output dimensions. I know PCA is not a good approach to spectral data. I usually go with NMF, evaluated with ...

ABSTRACT: To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models.

Discover how algorithms influence what kids watch and why it’s important to stay aware. Children wonder how their favorite videos are always next in line. Enter an adult who explains algorithms — ...