Recurrent neural networks are the foundation of many sequence-to-sequence models in
machine learning, such as machine translation and speech synthesis. With applied quantum …

Post-classification with multi-temporal remote sensing images is one of the most popular
change detection methods, providing the detailed “from-to” change information in real …

Change detection was one of the earliest and is also one of the most important applications
of remote sensing technology. For multispectral images, an effective solution for the change …

Nonstationary variations widely exist in abnormal industrial processes, in which the mean
values and variances of the fault nonstationary variables change with time. Thus, the …

Multitemporal imagery analysis has attracted widespread interest in recent years due to the
large number of applications. Multitemporal remote sensing imagery analysis is very …

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear
dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised …

For the process data analytics, numerous statistical methods are designed to extract
informative features to reveal latent characteristics and correlation patterns among the …

Quantum machine learning carries the promise to revolutionize information and
communication technologies. While a number of quantum algorithms with potential …

Quantum algorithms for data analysis Page 1 Quantum algorithms for data analysis
Alessandro Luongo 2023-05-30 Page 2 2 Page 3 Contents 1 Preface 7 1.1 Abstract …

Supervised learning from high-dimensional data, for example, multimedia data, is a
challenging task. We propose an extension of slow feature analysis (SFA) for supervised …