Results 31 to 40 of about 154,546 (314)
PLoS ONE, 2022 In the compressed processing of hyperspectral images, orthogonal matching pursuit algorithm (OMP) can be used to obtain sparse decomposition results.
Li Wang, Wei Wang
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Input Dependent Sparse Gaussian Processes
CoRR, 2021 Gaussian Processes (GPs) are Bayesian models that provide uncertainty estimates associated to the predictions made. They are also very flexible due to their non-parametric nature. Nevertheless, GPs suffer from poor scalability as the number of training instances N increases. More precisely, they have a cubic cost with respect to $N$.
Bahram Jafrasteh +2 more
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Sparse model construction using coordinate descent optimization [PDF]
, 2013 We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models.
Xia Hong +8 more
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Comparative Analysis of Sparse Matrix Algorithms For Information Retrieval [PDF]
Journal of Systemics, Cybernetics and Informatics, 2003 We evaluate and compare the storage efficiency of different sparse matrix storage formats as index structure for text collection and their corresponding sparse matrixvector multiplication algorithm to perform query processing in information retrieval (IR)
Nazli Goharian, Ankit Jain, Qian Sun
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Sparse Deconvolution Using Support Vector Machines
EURASIP Journal on Advances in Signal Processing, 2008 Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them ...
Aníbal R. Figueiras-Vidal +5 more
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Remote Sensing, 2021 In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we ...
Nicomino Fiscante +5 more
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PCGen: A Fully Parallelizable Point Cloud Generative Model
SensorsGenerative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing.
Nicolas Vercheval +3 more
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International Journal of Distributed Sensor Networks, 2020 Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing ...
Hongchao Wang, Wenliao Du
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3D SAR Imaging Method Based on Learned Sparse Prior
Leida xuebao, 2023 The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency.
Mou WANG +5 more
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Sparse Modeling for Image and Vision Processing [PDF]
Foundations and Trends® in Computer Graphics and Vision, 2014 In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection—that is, automatically selecting a simple model among a large collection of them.
Julien Mairal +2 more
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