Daniele Marmiroli, PhD

💨 Welcome you to submit your paper in the Special Issue: Computational Mathematics and Artificial Intelligence https://buff.ly/3UVZrKb #efficientnumerical #computationalcomplexity #large-scaleoptimization, #algorithms #MDPIOpenAccess #ComSciMath_Mdpi

3

Learn the basics of nonlinear dimensionality reduction, kernel PCA, and multidimensional scaling by following along Biman Chakraborty's clear, hands-on Python tutorial.

18

Following my latest post on Principal Component Analysis (PCA), https://lnkd.in/ez7erX8G, several people contacted me to get insights on the way to manage missing data in this context. So here is a textbook recommendation on the topic and more! 🔔 The textbook covers new cutting-edge topics in PCA, with a focus on open problems and in-depth analysis of PCA topics not covered anywhere else. Some R code inside. 📌 Sparse Principal Component Analysis via Rotation and Truncation 📌PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images 📌Principal Component Analysis in the Presence of Missing Data 📌Robust PCAs and PCA Using Generalized Mean 📌Principal Component Analysis Techniques for Visualization of Volumetric Data 📌Outlier-Resistant Data Processing with L1-Norm Principal Component Analysis 📌Damage and Fault Detection of Structures Using Principal 📌Component Analysis and Hypothesis Testing 📌Principal Component Analysis for Exponential Family Data 📌Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability Do not hesitate to share your applications of PCA to complex problems. We have been offering training sessions on multivariate statistics for over 3 decades now: https://lnkd.in/egYPDnFw https://lnkd.in/eFyh6Wpa #PCA #multivariate #statistics #datascience

65

2 Kommentare

This paper studies a #remote #monitoring #system in which a mutually independent and #heterogeneous #collection of finite-state irreducible #continuous #time #Markov #chain (#CTMC) based information sources is considered. In this system, a common remote monitor queries the instantaneous states of the #individual #CTMCs according to a Poisson process with possibly different intensities across the sources, in order to maintain #accurate #estimates of the original sources. Three information freshness models are considered to quantify the accuracy of the remote estimates: #fresh #when #equal (#FWE),# fresh #when #sampled (#FWS) and #fresh #when #close (#FWC). For each of these freshness models, closed-form expressions are derived for mean information freshness for a given source.----Nail Akar, @Sennur Ulukus More details can be found at this link: https://lnkd.in/gmXRmVmK

1

> sharing resource <  I will continue with Quantum Graph Neural Networks today with a the paper: "From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks" by Andrea Ceschini, Francesco Mauro, Francesca De Falco, Alessandro Sebastianelli, Alessio Verdone, Antonello Rosato, Bertrand Le Saux, Massimo Panella, Paolo Gamba, and Silvia Liberata Ullo Abstract: Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing data with complex relational structures but suffer from limitations such as high computational complexity and over-smoothing in large-scale applications. Quantum computing, leveraging principles like superposition and entanglement, offers a pathway to enhanced computational capabilities. This paper critically reviews the state-of-the-art in QGNNs, exploring various architectures. We discuss their applications across diverse fields such as high-energy physics, molecular chemistry, finance and earth sciences, highlighting the potential for quantum advantage. Additionally, we address the significant challenges faced by QGNNs, including noise, decoherence, and scalability issues, proposing potential strategies to mitigate these problems. This comprehensive review aims to provide a foundational understanding of QGNNs, fostering further research and development in this promising interdisciplinary field. Link: https://lnkd.in/dmudN-rC #quantummachinelearning #quantumcomputing #gnn #research #paper

118

6 Kommentare