Results 51 to 60 of about 24,916,518 (300)
Using the coefficient of variation to improve the sparsity of seismic data [PDF]
2013 IEEE Global Conference on Signal and Information Processing, 2013 In this work we propose using the coefficient of variation as a cost function to improve seismic data representation in the curvelet domain. Performance improvement is demonstrated in denoising and compressed sensing data recovery. The demonstrated approach can be extended to other seismic applications and alternate transforms.
Hasan Al-Marzouqi, Ghassan Al-Regib
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SparseBeads data: benchmarking sparsity-regularized computed tomography [PDF]
, 2017 Sparsity regularization (SR) such as total variation (TV) minimization allows accurate image reconstruction in x-ray computed tomography (CT) from fewer projections than analytical methods.
Withers, Philip; id_orcid +19 more
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Correlation Measures in Metagenomic Data: The Blessing of Dimensionality
Applied SciencesMicrobiome analysis has revolutionized our understanding of various biological processes, spanning human health and epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies.
Alessandro Fuschi +5 more
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Rule-Based Effective Collaborative Recommendation Using Unfavorable Preference
IEEE Access, 2020 The biggest challenge in collaborative filtering recommendation system research is data sparsity; it mainly occurs as user rates very few items from widely available options.
G. Suganeshwari, S. P. Syed Ibrahim
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Alleviating data sparsity for Twitter sentiment analysis [PDF]
, 2012 Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-character limit.
Saif, Hassan, He, Yulan, Alani, Harith
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On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter [PDF]
Proceedings of the Language Resources and Evaluation Conference, 2014 Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification.
Saif, Hassan +3 more
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Sparsity-based evolutionary multi-objective feature selection for multi-label classification
, 2021 Feature selection (FS) is typically an essential pre-processing step for many machine learning tasks. However, most existing FS approaches focus on single-label classification, whereas multi-label classification (MLC) is an emerging topic where each ...
Mengjie Zhang (8509230) +3 more
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FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation
IEEE Access, 2021 Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix.
Haiyang Zhang +4 more
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, 2019 Graph illustrating the convergence curves for FD+DCT sparsity, FD-sparsity, and DCT-sparsity-based penalties for the case of 40 spokes in Fig 5.
Abhijit J. Chaudhari (7406666) +9 more
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Leveraging Sparsity for Efficient Submodular Data Summarization
CoRR, 2017 The facility location problem is widely used for summarizing large datasets and has additional applications in sensor placement, image retrieval, and clustering. One difficulty of this problem is that submodular optimization algorithms require the calculation of pairwise benefits for all items in the dataset.
Erik M. Lindgren +2 more
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