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A Country That Never Sleeps? A Web Scrapping Analysis of the 24‐h Economy Policy in Ghana
Thunderbird International Business Review, EarlyView.ABSTRACT In light of revitalizing Ghana's economic landscape through sustainable job creation underpinned by 24‐h operations across all key sectors, the National Democratic Congress proposed the ‘24‐h economy’ policy proposal. This study employs the web‐scraping technique through text mining and python codes to analyse 1820 comments from Facebook, X ...
Pius Gamette +3 more
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Alternating Proximal Regularized Dictionary Learning
Neural Computation, 2014We present an algorithm for dictionary learning that is based on the alternating proximal algorithm studied by Attouch, Bolte, Redont, and Soubeyran ( 2010 ), coupled with a reliable and efficient dual algorithm for computation of the related proximity operators.
SALZO, SAVERIO +3 more
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Multi-Modal Convolutional Dictionary Learning
IEEE Transactions on Image Processing, 2022Convolutional dictionary learning has become increasingly popular in signal and image processing for its ability to overcome the limitations of traditional patch-based dictionary learning. Although most studies on convolutional dictionary learning mainly focus on the unimodal case, real-world image processing tasks usually involve images from multiple ...
Fangyuan Gao +4 more
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Parsimonious dictionary learning
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009Sparse modeling of signals has recently received a lot of attention. Often, a linear under-determined generative model for the signals of interest is proposed and a sparsity constraint imposed on the representation. When the generative model is not given, choosing an appropriate generative model is important, so that the given class of signals has ...
Mehrdad Yaghoobi +2 more
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2021
Dictionary learning is one of classical data-driven ways for linear feature extraction, which finds wide applications in image recovery and classification, audio processing, biomedical signal processing, and data fusion. As its natural extension for multidimensional data, tensor dictionary learning can extract the multilinear features. The optimization
Yipeng Liu, Jiani Liu, Zhen Long, Ce Zhu
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Dictionary learning is one of classical data-driven ways for linear feature extraction, which finds wide applications in image recovery and classification, audio processing, biomedical signal processing, and data fusion. As its natural extension for multidimensional data, tensor dictionary learning can extract the multilinear features. The optimization
Yipeng Liu, Jiani Liu, Zhen Long, Ce Zhu
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2018
Sparse representations are linear by construction, a fact that can hinder their use in classification problems. Building vectors of characteristics from the signals to be classified can overcome the difficulties and is automated by employing kernels, which are functions that quantify the similarities between two vectors.
Bogdan Dumitrescu, Paul Irofti
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Sparse representations are linear by construction, a fact that can hinder their use in classification problems. Building vectors of characteristics from the signals to be classified can overcome the difficulties and is automated by employing kernels, which are functions that quantify the similarities between two vectors.
Bogdan Dumitrescu, Paul Irofti
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2017
The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semi-supervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively.
Sheng Li, Yun Fu
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The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semi-supervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively.
Sheng Li, Yun Fu
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2018
Dictionary learning can be formulated as an optimization problem in several ways. We present here the basic form, where the representation error is minimized under the constraint of sparsity, and discuss several views and relations with other data analysis and signal processing problems. We study some properties of the DL problem and their implications
Bogdan Dumitrescu, Paul Irofti
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Dictionary learning can be formulated as an optimization problem in several ways. We present here the basic form, where the representation error is minimized under the constraint of sparsity, and discuss several views and relations with other data analysis and signal processing problems. We study some properties of the DL problem and their implications
Bogdan Dumitrescu, Paul Irofti
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Leveraging seed dictionaries to improve dictionary learning
2016 IEEE International Conference on Image Processing (ICIP), 2016Most state-of-the-art dictionary learning algorithms (DLAs) are iterative, and must begin with an initial estimate of the dictionary, referred to as the seed. A seed can be generated randomly, but it has been shown that choosing a more intelligent seed often yields a better solution.
Daniel Reichman +2 more
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Dictionary Reduction: Automatic Compact Dictionary Learning for Classification
2017A complete and discriminative dictionary can achieve superior performance. However, it also consumes extra processing time and memory, especially for large datasets. Most existing compact dictionary learning methods need to set the dictionary size manually, therefore an appropriate dictionary size is usually obtained in an exhaustive search manner. How
Yang Song +4 more
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