Results 21 to 30 of about 1,083,294 (265)
Enhanced PSSV for Incomplete Data Analysis
IEEE Access, 2020 Partial Sum Minimization of Singular Values (PSSV) is a powerful tool for image denoising, matrix completion and recovering underlying low-rank structure from the corrupted data via Partial Sum Minimization of Singular Values. However, the performance of
Weimin Hou, Qin Li
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Profile Likelihood and Incomplete Data [PDF]
International Statistical Review, 2010 Summary According to the law of likelihood, statistical evidence is represented by likelihood functions and its strength measured by likelihood ratios. This point of view has led to a likelihood paradigm for interpreting statistical evidence, which carefully distinguishes evidence about a parameter from error probabilities and personal belief.
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Comparison of Algorithms for Clustering Incomplete Data
Foundations of Computing and Decision Sciences, 2014 The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data.
Matyja Artur, Siminski Krzysztof
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Phase Identification With Incomplete Data
IEEE Transactions on Smart Grid, 2018 Phase identification is a process to determine which of the three phases a particular house is connected to. The state-of-the-art identification methods usually exploit smart metering data. However, the data sets are not always available and the major challenge is hence to identify phases with incomplete data set.
Minghao Xu, Ran Li 0004, Furong Li 0004
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Sensors, 2018 In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values.
Yang Zhang +4 more
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Applied Sciences, 2022 Feature selection has been widely used in machine learning and data mining since it can alleviate the burden of the so-called curse of dimensionality of high-dimensional data.
Jun Cai, Linge Fan, Xin Xu, Xinrong Wu
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Textually Summarising Incomplete Data [PDF]
Proceedings of the 10th International Conference on Natural Language Generation, 2017 Many data-to-text NLG systems work with data sets which are incomplete, ie some of the data is missing. We have worked with data journalists to understand how they describe incomplete data, and are building NLG algorithms based on these insights. A pilot evaluation showed mixed results, and highlighted several areas where we need to improve our system.
Stephanie Inglis +2 more
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Correlating variables with different scale types: A new framework based on matrix comparisons
Methods in Ecology and Evolution, 2023 Ecological variables may be expressed on four basic measurement scales (nominal, ordinal, interval or ratio), whereas circular variables and those combining a nominal state with other scale types are also common.
János Podani +2 more
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Uncovering Suspicious Activity From Partially Paired and Incomplete Multimodal Data
IEEE Access, 2017 Multimodal data can be used to gain additional perspective on a phenomenon. For applications, such as security and the detection of suspicious activity, the need to aggregate and analyze data from multiple modes is vital.
Carter Chiu, Justin Zhan, Felix Zhan
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On econometric modeling of incomplete data [PDF]
Methods of Operations Research, 1985 We discuss results on identification and estimation of dynamic models when values of the endogenous variable are regularly missing. The available data are assumed to be sampled at regular intervals of length k and can be linear combinations of the realizations of the variable over a finite number of periods.
Nijman, T.E., Palm, F.C.
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