Results 11 to 20 of about 24,916,518 (300)
Morphological Diversity and Sparsity for Multichannel Data Restoration [PDF]
Journal of Mathematical Imaging and Vision, 2008 Over the last decade, overcomplete dictionaries and the very sparse signal representations they make possible, have raised an intense interest from signal processing theory. In a wide range of signal processing problems, sparsity has been a crucial property leading to high performance. As multichannel data are of growing interest, it seems essential to
Bobin, Jérome +3 more
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Mitigating Data Sparsity in Integrated Data through Text Conceptualization [PDF]
2024 IEEE 40th International Conference on Data Engineering (ICDE)We study the data sparsity problem for data generated from an integration system. We approach the problem from a textual information extraction perspective and propose to conceptualize external documents using the concepts in the integrated schema. We present THOR, a novel system that, unlike related approaches, neither relies on complex rules nor ...
Rahman, Md Ataur +3 more
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Algorithms to Exploit Data Sparsity
, 2021 While data in the real world is very high-dimensional, it generally has some underlying structure; for instance, if we think of an image as a set of pixels with associated color values, most possible settings of color values correspond to something more like random noise than what we typically think of as a picture.
Flodin, Larkin H
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Group and Graph Joint Sparsity for Linked Data Classification
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 Various sparse regularizers have been applied to machine learning problems, among which structured sparsity has been proposed for a better adaption to structured data. In this paper, motivated by effectively classifying linked data (e.g.
Longwen Gao, Shuigeng Zhou
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Accounting for data sparsity when forming spatially coherent zones. [PDF]
Appl Math Model, 2019 Efficient farm management can be aided by the identification of zones in the landscape. These zones can be informed from different measured variables by ensuring a sense of spatial coherence.
Hassall KL, Whitmore AP, Milne AE.
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Explaining adversarial vulnerability with a data sparsity hypothesis [PDF]
Neurocomputing, 2022 Despite many proposed algorithms to provide robustness to deep learning (DL) models, DL models remain susceptible to adversarial attacks. We hypothesize that the adversarial vulnerability of DL models stems from two factors. The first factor is data sparsity which is that in the high dimensional input data space, there exist large regions outside the ...
Mahsa Paknezhad +6 more
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Overcoming Data Sparsity in Group Recommendation [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 It has been an important task for recommender systems to suggest satisfying activities to a group of users in people's daily social life. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group.
Hongzhi Yin +4 more
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Stealth Data Injection Attacks With Sparsity Constraints
IEEE Transactions on Smart Grid, 2023 Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution that aims to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the ...
Xiuzhen Ye +3 more
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Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit [PDF]
, 2014 We investigate the methods that simultaneously enforce sparsity and low-rank structure in a matrix as often employed for sparse phase retrieval problems or phase calibration problems in compressive sensing.
Gribonval, Rémi +3 more
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Spectral Unmixing via Data-Guided Sparsity [PDF]
IEEE Transactions on Image Processing, 2014 Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization and understanding. From an unsupervised learning perspective, this problem is very challenging---both the spectral bases and their composite ...
Feiyun Zhu +5 more
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