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Identifiability of Complete Dictionary Learning [PDF]
SIAM Journal on Mathematics of Data Science, 2019 Sparse component analysis (SCA), also known as complete dictionary learning, is the following problem: Given an input matrix $M$ and an integer $r$, find a dictionary $D$ with $r$ columns and a matrix $B$ with $k$-sparse columns (that is, each column of $B$ has at most $k$ non-zero entries) such that $M \approx DB$.
Cohen, Jérémy, Gillis, Nicolas
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Denoising of Seismic Data Based on Block Dictionary Learning Theory
CT Lilun yu yingyong yanjiu, 2022 With the increasingly complex observation environment of oil and gas exploration, the seismic data collected are often mixed with various noise signals, resulting in the effective weak signal caused by the exploration target is covered, which seriously ...
Junjie ZHOU +3 more
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Task-Driven Dictionary Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012 Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks.
Mairal, Julien +2 more
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Online Graph Dictionary Learning
, 2021 International audienceDictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements.
Flamary, Rémi +4 more
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DEVELOPING E-DICTIONARY AS AN INNOVATIVE MEDIA IN COVID-19 PANDEMIC
Celtic, 2021 Developing e-dictionary as an innovative online learning media in Covid-19 pandemic is a smart option in learning. It was needed to use in teaching English both online and offline.
Fitria Nur Hamidah +2 more
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Learning of structured graph dictionaries [PDF]
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012 We propose a method for learning dictionaries towards sparse approximation of signals defined on vertices of arbitrary graphs. Dictionaries are expected to describe effectively the main spatial and spectral components of the signals of interest, so that their structure is dependent on the graph information and its spectral representation. We first show
Zhang, X, Dong, X, Frossard, P
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Improved Cross-Label Suppression Dictionary Learning for Face Recognition
IEEE Access, 2018 Cross-label suppression dictionary learning is an effective approach to preserve the label property for signal representation in face recognition. This paper presents a proposed improved dictionary learning algorithm, considering the tradeoffs between ...
Tian Zhou +4 more
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Supervised Dictionary Learning
CoRR, 2008 It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones.
Mairal, Julien +4 more
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IEEE Access, 2019 Analysis-synthesis dictionary pair learning, which can provide a comprehensive view of data representation, has been applied in various computer vision tasks.
Heyou Chang +4 more
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Multi-task hybrid dictionary learning for vehicle classification in sensor networks
International Journal of Distributed Sensor Networks, 2018 In this article, we propose a novel multi-task hybrid dictionary learning approach for moving vehicle classification tasks using multi-sensor networks to improve the classification accuracy in complex scenes with low time complexity, which considers both
Rui Wang +3 more
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