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Informative Feature Extraction
2021Laser molecular imaging produces high-dimension data with the structure dependent on the optical modality, laser type, detection method, kind of sample, etc. Generally, data’s high dimension corresponds to a situation where the number of initial parameters exceeds by orders of magnitude the number of hidden independent variables, e.g., when the number ...
Denis A. Vrazhnov +2 more
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INFORMATION AND LAW, 2011
У статті висвітлюються особливості правової інформації.
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У статті висвітлюються особливості правової інформації.
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Feature Selection via Vectorizing Feature’s Discriminative Information
2016Feature selection is a popular technology for reducing dimensionality. Commonly features are evaluated with univariate scores according to their classification abilities, and the high-score ones are preferred and selected. However, there are two flaws for this strategy. First, feature complementarity is ignored.
Jun Wang, Hengpeng Xu, Jinmao Wei
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Feature information extraction from dynamic biospeckle
Applied Optics, 1994Dynamic biospeckle can be obtained from cultures of motile microorganisms by use of a hybrid opto-electronic system, and it carries feature information about the cultures. On the basis of theoretical analysis and experimental results we describe and discuss two techniques that process the signals derived from dynamic biospeckle. Experimental results on
B, Zheng, C M, Pleass, C S, Ih
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Unsupervised Feature Selection: Minimize Information Redundancy of Features
2010 International Conference on Technologies and Applications of Artificial Intelligence, 2010This paper proposes an unsupervised feature selection method to remove the redundant features from datasets. The major contributions are twofold. First, we propose an eigen-decomposition method to rank the hyperplanes (which describes the relations between features) based on their linear dependency characteristic, and then design an efficient Gaussian ...
Chun-Chao Yen +2 more
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Mutual Information Criteria for Feature Selection
2011In many data analysis tasks, one is often confronted with very high dimensional data. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. To overcome this problem it is frequently assumed either that features independently influence the class variable or do so only involving pairwise ...
Zhang Z., Hancock E.R.
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Information based universal feature extraction
SPIE Proceedings, 2015In many real world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, they mostly remain task-specific, although humans who perform such a task always use the same image features, trained in early childhood.
Mohammad Amiri, Rüdiger Brause
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An information-theoretic SAR heterogeneity feature
2004In this work, a heterogeneity feature, calculable from SAR images on a per-pixel basis, but relying on global image statistics, is described and discussed. Starting from the multiplicative speckle and texture models relating the amount of texture and speckle to the local mean and variance at every pixel, such a feature is rigorously derived from ...
B Aiazzi, L Alparone, S Baronti
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Discriminating features and information theory
Annual Meeting Optical Society of America, 1985We assume that visual recognition is accomplished by describing an object using visual features and that description is matched to some stored data base. These features must be chosen from the set of all computable properties of an image, be they direct image properties or properties of the actual object inferred from the image. What criteria should be
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