Abstract: In this paper, we analyze Xu and Yuille’s robust principal component analysis (RPCA) learning algorithms by means of the distance measurement in space. Based on the analysis, a family of ...
Correspondence to Dr Lionel Spielmann, Service de Rhumatologie, Hospices Civils de Colmar, Colmar 68024, Alsace (Région), France; lionel.spielmann{at}ch-colmar.fr We thank Pinal-Fernandez and Mammen ...
ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...
Fei Li, Associate Professor, Computer Science, College of Engineering and Computing (CEC), received funding for the project: “Quantum Algorithms for High-Performance Analysis of Single-Cell Omics Data ...
3DPrinterOS is working with MIX Lab at Montclair State University to develop an algorithm that can identify 3D printed guns. Additively manufactured firearms remain a concern for law enforcement ...
A GUI-based Tic-Tac-Toe game built with Python and Tkinter. Play in Single Player mode against an AI powered by the Minimax algorithm or challenge a friend in Multiplayer mode. Features include ...
The Hydrological cycle, also known as water cycle, involves the continuous circulation of water in the Earth-atmosphere system. Accurate measurements of various hydrological cycle components (e.g.
Abstract: Dimensionality reduction is an essential preprocessing step for data mining. Principal component analysis (PCA) is the most classical method of reducing dimension and a variety of methods ...
ABSTRACT: The speckle noise is considered one of the main causes of degradation in ultrasound image quality. Many despeckling filters have been proposed, which are always making a trade-off between ...
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 ...
Principal component analysis (PCA) is a popular method for modeling and analysis of high-dimensional data. In spite of its advantages, classical PCA also has two drawbacks. First, it is very sensitive ...
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