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Identifier 000456443
Title Tensor signal modeling for high-dimensional deep learning systems
Alternative Title Μοντελοποίηση σημάτων τανυστών για συστήματα βαθιάς μάθησης ανώτερων διαστάσεων
Author Γιαννόπουλος, Μιχαήλ Ν.
Thesis advisor Τσακαλίδης, Παναγιώτης
Reviewer Αργυρός, Αντώνιος
Τζαγκαράκης, Γεώργιος
Ζερβάκης, Μιχάλης
Τραχανιάς, Πάνος
Χρυσουλάκης, Νεκτάριος
Τριανταφύλλου, Σοφία
Abstract The information explosion triggered by the creation of vast amounts of data anytime, anywhere, around the globe implies that we are certainly living in an era of data deluge. Although traditional flat-view matrix modeling methods are usually suitable for their processing, when data dimensionality scales up as a direct consequence of their multi-factor creation mechanisms, their limitations become apparent and may jeopardize subsequent efficient processing. This calls for the design of modern high-dimensional tensor-based methods and frameworks, tailored especially to exploit existing correlations in the inherent dimensions of the data. The prevailing contribution of this thesis is therefore to develop a novel framework for processing high-dimensional data, as well as to show its practical utility in several applications of signal processing and machine learning. Towards that end, in the first part of this dissertation we initially consider the fundamental concepts of modeling and processing high-dimensional data, namely tensors and their decompositions. These higher-order processing workhorses, made it feasible to model, treat and process efficiently high dimensional data in real-world problems, in the quest of deriving new results in various application domains of interest. In particular, we adopted tensor-based approaches for efficiently imputing missing measurements and compared them with state-of-the-art matrix-based ones in the context of supervised classification. In that way, we were able to prove that concrete merits are made available when higher-order tensor processing structures are employed, and hence high-dimensional data processing in their nominal dimensions paves the way for clearly enhanced performance results. Since deep learning approaches have dominated the machine learning field in several interesting low and high-dimensional tasks, in the second part of this thesis we initially elucidate their theoretical foundations in order to familiarize with their key processing mechanisms to be used in upcoming applications. We focus on convolutional neural networks, specially tailored for imaging tasks, by reviewing every building block of them to be used and extended in this thesis. Subsequently, we cope with several lower-order supervised learning applications of signal processing flavor, by formulating the respective problems at hand as instances of low-order convolutional neural networks classification tasks. Throughout this process we were able to show that low-order convolutional neural networks are excellent feature learners as the dimensionality of their input data scales up, and, hence, their extension for addressing higher-order supervised learning tasks is worthwhile. Combining the theoretical and the application-based intuition obtained from the aforementioned parts, the last part of the dissertation focuses on extending existing low-order convolutional neural networks to their higher-order analogues, in order to efficiently cope with high-dimensional supervised learning regimes arising in practice. More precisely, we introduce the notion of N-dimensional convolutional neural networks, by extending the key concept of convolution to the general case. The respective extension is performed via tensor decompositions as well as stacked convolutions, with the pros and cons of each method being highlighted towards their integration in modern deep learning systems. Capitalizing on the designed core-functionality extension, we subsequently verify its performance in several inherently high-dimensional supervised machine learning tasks in remote sensing, ranging from classification to regression. The obtained experimental results clearly demonstrate that tensor signal modeling in conjunction with convolutional neural networks architectures offer concrete merits towards efficient high-dimensional data processing and learning in the context of novel deep learning systems.
Language English
Subject Convolutional neural networks
Signal processing
Tensor decompositions
Επεξεργασία σήματος
Παραγοντοποιήσεις τανυστών
Συνελικτικά νευρωνικά δίκτυα
Issue date 2023-07-21
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
Permanent Link https://elocus.lib.uoc.gr//dlib/4/0/7/metadata-dlib-1687255899-74167-10040.tkl Bookmark and Share
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