Results 21 to 30 of about 601,504 (262)
Compressing Fisher Vector for Robust Face Recognition
IEEE Access, 2017 One major topic for robust face recognition could be the efficient encoding of facial descriptors. Among various encoders, Fisher vector (FV) is one of the probabilistic methods that yield promising results.
Hongjun Wang, Jiani Hu, Weihong Deng
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Nonlinear dimensionality reduction on graphs [PDF]
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while
Yanning Shen +2 more
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Adaptive Metric Dimensionality Reduction [PDF]
Theoretical Computer Science, 2013 We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces. Our main statistical contribution is a generalization bound for Lipschitz functions in metric spaces that are doubling, or nearly doubling.
Lee-Ad Gottlieb +2 more
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Factorization and regularization by dimensional reduction [PDF]
Physics Letters B, 2005 Since an old observation by Beenakker et al, the evaluation of QCD processes in dimensional reduction has repeatedly led to terms that seem to violate the QCD factorization theorem. We reconsider the example of the process gg->ttbar and show that the factorization problem can be completely resolved.
Signer, A., Stöckinger, D.
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Emergence of time persistence in a data-driven neural network model
eLife, 2023 Establishing accurate as well as interpretable models of network activity is an open challenge in systems neuroscience. Here, we infer an energy-based model of the anterior rhombencephalic turning region (ARTR), a circuit that controls zebrafish swimming
Sebastien Wolf +4 more
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Transferred Dimensionality Reduction [PDF]
, 2008 Dimensionality reduction is one of the widely used techniques for data analysis. However, it is often hard to get a demanded low-dimensional representation with only the unlabeled data, especially for the discriminative task. In this paper, we put forward a novel problem of Transferred Dimensionality Reduction, which is to do unsupervised ...
Zheng Wang 0010 +2 more
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Energies, 2020 Thickness of tectonically deformed coal (TDC) has positive correlations with the susceptible gas outbursts in coal mines. To predict the TDC thickness of the coalbed, we proposed a prediction method using seismic attributes based on the deep belief ...
Xin Wang, Tongjun Chen, Hui Xu
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Dynamics of dimensional reduction
Physics Letters B, 1980 In d-dimensional unified theories that, along with gravity, contain an antisymmetric tensor field of rank s-1, preferential compactification of d-s or of s space-like dimensions is found to occur. This is the case in 11-dimensional supergravity where s = 4.
Peter G.O. FREUND, Mark A. RUBIN
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AN ALTERNATIVE DIMENSIONAL REDUCTION PRESCRIPTION [PDF]
Modern Physics Letters A, 1996 We propose an alternative dimensional reduction prescription which in respect with Green functions corresponds to dropping the extra spatial coordinate. From this, we construct the dimensionally reduced Lagrangians both for scalars and fermions, discussing bosonization and supersymmetry in the particular two-dimensional case.
Edelstein Glaubach, José Daniel +3 more
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IEEE Open Journal of Instrumentation and Measurement, 2022 Deep learning neural network serves as a powerful tool for visual anomaly detection (AD) and fault diagnosis, attributed to its strong abstractive interpretation ability in the representation domain.
Jie Lin, Song Chen, Enping Lin, Yu Yang
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