Results 271 to 280 of about 6,486,492 (310)
Magnetic Resonance in Medicine, EarlyView.ABSTRACT Purpose To develop, optimize, and characterize a respiratory motion‐resolved free‐running isotropic 3D carotid vessel wall T1 mapping technique named CARISMAT1C. Methods An inversion‐recovery gradient‐echo free‐running pulse sequence with interleaved double flip‐angle (2FA) was implemented, and an extended‐phase‐graph dictionary was used to ...
Isabel Montón Quesada +7 more
wiley +1 more source
Multi-separable dictionary learning
Signal Processing, 2018 Abstract As the extensive applications of sparse representation, the methods of dictionary learning have received widespread attentions. In this paper, we propose a multi-separable dictionary learning (MSeDiL) algorithm for sparse representation, which is based on the Lagrange Multiplier and the QR decomposition.
Fengzhen Zhang +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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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.
Daniël Reichman 0002 +2 more
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Attribute Guided Dictionary Learning
Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, 2015Attributes have shown great potential in visual recognition recently since they, as mid-level features, can be shared across different categories. However, existing attribute learning methods are prone to learning the correlated attributes which results in the difficulties of selecting attribute specific features. In this paper, we propose an attribute
Wang, Wei, Yan, Yan, Sebe, Niculae
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Malware Identification with Dictionary Learning
2019 27th European Signal Processing Conference (EUSIPCO), 2019Malware identification is a difficult task that has been recently approached by training classifiers through machine learning. We present here a low complexity semi-supervised dictionary learning framework that begins with training an initial dictionary on a small labeled data set, and then continues with online learning on incoming unlabeled data ...
Paul Irofti, Andra Baltoiu
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Online Dictionary Learning with Confidence
2018 IEEE International Conference on Data Mining (ICDM), 2018Online dictionary learning has received intensive attention in signal processing field with streaming or dynamic data. Different from classical online dictionary learning methods that treat all atoms equally, in this paper, we present a novel online dictionary learning with a confidence parameter introduced on each of atoms.
Shan You, Chang Xu 0002, Chao Xu 0006
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Siamese Deep Dictionary Learning
2019 International Joint Conference on Neural Networks (IJCNN), 2019Researchers have explored the importance of Siamese networks in deep learning. With recent developments in deep learning and the effectiveness of deep dictionary learning, this research proposes the architecture of Siamese Deep Dictionary Learning. We first propose the architecture followed by solving the optimization problem.
Vanika Singhal +3 more
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Dictionary Learning in Texture Classification
2011Texture analysis is used in numerous applications in various fields. There have been many different approaches/techniques in the literature for texture analysis among which the texton-based approach that computes the primitive elements representing textures using k-means algorithm has shown great success. Recently, dictionary learning and sparse coding
Mehrdad J. Gangeh +2 more
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Discriminative Analysis Dictionary Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2016Dictionary learning (DL) has been successfully applied to various pattern classification tasks in recent years. However, analysis dictionary learning (ADL), as a major branch of DL, has not yet been fully exploited in classification due to its poor discriminability.
Jun Guo 0008 +4 more
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