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QUALITY ASSESSMENT OF DIMENSIONALITY REDUCTION TECHNIQUES ON HYPERSPECTRAL DATA: A NEURAL NETWORK BASED APPROACH [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Dimensionality reduction of hyperspectral images plays a vital role in remote sensing data analysis. The rapid advances in hyperspectral remote sensing has brought in a lot of opportunities to researchers to come up with advanced algorithms to analyse ...
C. Deepa, A. Shetty, A. V. Narasimhadhan
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, 2023 [en] In this project we study antoencoders, a machine learning tecnique used for dimensionality reduction of databases, analizing images or generating new data. We compare them with tradicional dimensionality reduction method, the principal component analysis (PCA).
Christopher M. Bishop, Hugh Bishop
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Spiking Autoencoders With Temporal Coding
Frontiers in Neuroscience, 2021 Spiking neural networks with temporal coding schemes process information based on the relative timing of neuronal spikes. In supervised learning tasks, temporal coding allows learning through backpropagation with exact derivatives, and achieves ...
Iulia-Maria Comşa +3 more
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Biological Cybernetics, 2022 AbstractIn Bourlard and Kamp (Biol Cybern 59(4):291–294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called “auto-associative multilayer perceptrons”) were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value decomposition and least ...
Hervé Bourlard, Selen Hande Kabil
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MONITORING DATA AGGREGATION OF DYNAMIC SYSTEMS USING INFORMATION TECHNOLOGIES
Сучасний стан наукових досліджень та технологій в промисловості, 2023 The subject matter of the article is models, methods and information technologies of monitoring data aggregation. The goal of the article is to determine the best deep learning model for reducing the dimensionality of dynamic systems monitoring data ...
Dmytro Shevchenko +2 more
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An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications
Entropy, 2021 Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the ...
Aman Singh, Tokunbo Ogunfunmi
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FEATURE DESCRIPTOR BY CONVOLUTION AND POOLING AUTOENCODERS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 In this paper we present several descriptors for feature-based matching based on autoencoders, and we evaluate the performance of these descriptors. In a training phase, we learn autoencoders from image patches extracted in local windows surrounding key ...
L. Chen, F. Rottensteiner, C. Heipke
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Denoising Adversarial Autoencoders [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space.
Antonia Creswell, Anil Anthony Bharath
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Complex-valued autoencoders [PDF]
Neural Networks, 2012 Autoencoders are unsupervised machine learning circuits whose learning goal is to minimize a distortion measure between inputs and outputs. Linear autoencoders can be defined over any field and only real-valued linear autoencoder have been studied so far.
Baldi, Pierre, Lu, Zhiqin
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A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy
Frontiers in Computational Neuroscience, 2021 Over the last few decades, electroencephalogram (EEG) has become one of the most vital tools used by physicians to diagnose several neurological disorders of the human brain and, in particular, to detect seizures.
Ahmed Abdelhameed, Magdy Bayoumi
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