Results 31 to 40 of about 48,878 (307)
A Hierarchical Bayesian Model for Frame Representation [PDF]
, 2010 In many signal processing problems, it is fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyperparameters characterizing the probability distribution of the frame
Amel Benazza-Benyahia +9 more
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The effect of dictionary learning on weight update of AdaBoost and ECG classification
Journal of King Saud University: Computer and Information Sciences, 2020 A signal can be represented by sparse representation with fewer coefficients. Due to this ability, sparse representation is used in research fields such as signal compression, noise elimination, and classification.
Mücahid Barstuğan, Rahime Ceylan
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Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation [PDF]
, 2012 Sparse representations accurately model many real-world data sets. Some form of sparsity is conceivable in almost every practical application, from image and video processing, to spectral sensing in radar detection, to bio-computation and genomic signal ...
Khajehnejad, M. Amin
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Radar maneuvering target detection in clutter background should not only consider the complex characteristics of the target to accumulate its energy as much as possible, but also suppress clutter to improve the signal-to-clutter ratio (SCR).
Xiaolong Chen +3 more
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, 2013 With increasing applications of hyperspectral imagery (HSI) in agriculture, mineralogy, military, and other fields, one of the fundamental tasks is accurate detection of the target of interest.
Li, Xiaohui +3 more
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Block Orthonormal Overcomplete Dictionary Learning [PDF]
, 2013 In the field of sparse representations, the overcomplete dictionary learning problem is of crucial importance and has a growing application pool where it is used.
Dumitrescu, Bogdan, Rusu, Cristian
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Jixie chuandong, 2020 Sparse representation has a wide range of applications in the field of image processing and audio processing. Applying the sparse representation theory to the field of vibration signal processing can efficiently represent the periodic components of the ...
Xiaoyun Gong +3 more
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Parametric dictionary learning for topological signal representation
, 2023 The aim of this paper is to introduce a novel dictionary learning algorithm for sparse representation of signals defined over regular cell complexes. Leveraging tools from Hodge theory, we inject the underlying topology in the dictionary structure by ...
Di Lorenzo, Paolo +2 more
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Robust Sparse Representation for Incomplete and Noisy Data
Information, 2015 Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on ...
Jiarong Shi, Xiuyun Zheng, Wei Yang
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Shock and Vibration, 2020 The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing.
Zhan Xing +3 more
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