Matrix factorization techniques have become pivotal in data mining, enabling the extraction of latent structures from large-scale data matrices. These methods decompose complex datasets into ...

Abstract: Motivated by the success of recent deep learning researches on radar, we consider a deep learning based matrix factorization method for suppressing the high range-angle side-lobes in random ...

In statistical practice, rectangular tables of numeric data are commonplace, and are often analyzed using dimension-reduction methods like the singular value decomposition and its close cousin, ...

Abstract: Multi-view clustering (MVC) has gained attention for its ability to efficiently handle complex high-dimensional data. Many existing MVC methods rely on a technique known as Nonnegative ...

The rise of social media platforms has fundamentally altered the public discourse by providing easy to use and ubiquitous forums for the exchange of ideas and opinions. Elected officials often use ...

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...

This delivers what could be the most significant breakthrough in private AI training since differential privacy was first introduced.

Understanding how the brain learns and applies rules is the key to unraveling the neural basis of flexible behavior. A new ...

This repository provides implementations of various matrix completion algorithms based on convex optimization. Convex optimization is particularly useful in this context because it offers theoretical ...