Results 41 to 50 of about 24,916,518 (300)
SAR moving target imaging using group sparsity [PDF]
, 2013 SAR imaging of scenes containing moving targets results in defocusing in the reconstructed images if the SAR observation model used in imaging does not take the motion into account.
Önhon, Özben Naime, Çetin, Müjdat
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Sparsity and morphological diversity for hyperspectral data analysis [PDF]
2009 16th IEEE International Conference on Image Processing (ICIP), 2009 Recently morphological diversity and sparsity have emerged as new and effective sources of diversity for Blind Source Separation. Based on these new concepts, novelmethods such as Generalized Morphological Component Analysis have been put forward. The latter takes advantage of the very sparse representation of structured data in large overcomplete ...
Jérôme Bobin +3 more
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sparsity-pattern: Generate different types of sparsity pattern for sparse matrices.
, 2021 The packages offers the following types of sparsity pattern. (1) 'diag', a diagonal matrix, (2) 'nodiag', a dense matrix without main diagonal elements, (3) 'block', a block matrix, (4) 'circle', i.e.
Ulf Hamster
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Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution
IEEE Access, 2020 Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users.
Charinya Wangwatcharakul +1 more
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Leveraging User Comments for Recommendation in E-Commerce
Applied Sciences, 2020 Collaborative filtering recommender systems traditionally recommend products to users solely based on the given user-item rating matrix. Two main issues, data sparsity and scalability, have long been concerns.
Pang-Ming Chu +3 more
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DISTRIBUTED UNMIXING OF HYPERSPECTRAL DATAWITH SPARSITY CONSTRAINT [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Abstract. Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used ...
S. Khoshsokhan, R. Rajabi, H. Zayyani
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A content-location-aware personalized POI recommendation model
Shenzhen Daxue xuebao. Ligong ban, 2022 Aiming at the data sparsity problem of user-POI matrix in point of interest (POI) recommendation, the more and more studies have explored the contextual factors such as geographical location, content information and social relations to deal with the ...
LIANG Bi +3 more
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Sparsity based defense against adversarial examples: v1.0
, 2020 Sparsity-based defense against adversarial attacks on machine learning classifiers. Contains code for the following papers: S. Gopalakrishnan, Z. Marzi, U. Madhow, R.
Soorya Gopalakrishnan
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A sparsity-driven approach to multi-camera tracking in visual sensor networks [PDF]
, 2013 In this paper, a sparsity-driven approach is presented for multi-camera tracking in visual sensor networks (VSNs). VSNs consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth
Çetin, Müjdat +3 more
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IEEE AccessWhen object detection is carried out in settings with sparse and irregular data acquisition, conventional sequencing techniques that depend on continuous tracking or dense observations frequently fall short of reconstructing the proper logical sequence ...
Herrick Han Lin Yeap +3 more
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