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Curse of Dimensionality in Adversarial Examples
2019 International Joint Conference on Neural Networks (IJCNN), 2019While machine learning and deep neural networks in particular, have undergone massive progress in the past years, this ubiquitous paradigm faces a relatively newly discovered challenge, adversarial attacks. An adversary can leverage a plethora of attacking algorithms to severely reduce the performance of existing models, therefore threatening the use ...
Nandish Chattopadhyay +3 more
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Mass cytometry: blessed with the curse of dimensionality
Nature Immunology, 2016Immunologists are being compelled to develop new high-dimensional perspectives of cellular heterogeneity and to determine which applications best exploit the power of mass cytometry and associated multiplex approaches.
Evan W Newell, Yang Cheng
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Lifting the Curse of Dimensionality
2007In certain problem domains, “The Curse of Dimensionality” (Hastie et al., 2001) is well known. Also known as the problem of “High P and Low N” where the number of parameters far exceeds the number of samples to learn from, we describe our methods for making the most of limited samples in producing reasonably general classification rules from data with ...
W. P. Worzel, A. Almal, C. D. MacLean
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High dimensional neurofuzzy systems: overcoming the curse of dimensionality
Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, 2002Many researchers do not appreciate the problems in building high-dimensional fuzzy models or control surfaces, yet this task has occupied researchers in several fields for the past thirty years. The problems occur due to the lack of both available training data and the required computational resources necessary for building and calculating the response
M. Brown +3 more
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Curse of Dimensionality in Paradoxical High Dimensional Clinical Datasets – A Survey
International Journal of Business Intelligents, 2015Data storage and retrieval is one among the challenging process in the field of computation. The storage and retrieval of multi-dimensional unstructured conflict data are needs the conversion of structure process. The storage has the major impact on access and computation time.
S.Rajeswari Ms +2 more
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Using Randomization to Break the Curse of Dimensionality
Econometrica, 1997Summary: This paper introduces random versions of successive approximations and multigrid algorithms for computing approximate solutions to a class of finite and infinite horizon Markovian decision problems (MDPs). We prove that these algorithms succeed in breaking the ``curse of dimensionality'' for a subclass of MDPs known as discrete decision ...
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Splitting Enables Overcoming the Curse of Dimensionality
2016In this chapter we briefly outline a new and remarkably fast algorithm for solving a large class of high dimensional Hamilton-Jacobi (H-J) initial value problems arising in optimal control and elsewhere [1]. This is done without the use of grids or numerical approximations.
Jérôme Darbon, Stanley J. Osher
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High Dimensional Covariance Estimation: Avoiding the ‘Curse of Dimensionality’
1994A central problem in standard parametric applications of multivariate analysis is covariance estimation, particularly relatively stable and robust estimation of covariance matrices as well as their inverses to support standard inferential decisions and conc1usions.
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