Using Kernel Discriminant Analysis to Improve the Characterization of the Alternative Hypothesis for Speaker Verification

AbstractPairs of states, or “boxes” are the basic objects in the resource theory of asymmetric distinguishability (Wang and Wilde in Phys Rev Res 1(3):033170, 2019. 10.1103/PhysRevResearch.1.033170), where free operations are arbitrary quantum channels that are applied to both states. From this point of view, hypothesis testing is seen as a process by which a standard form of distinguishability is distilled. Motivated by the more general problem of quantum state discrimination, we consider boxes of a fixed finite number of states and study an extension of the relative submajorization preorder to such objects. In this relation, a tuple of positive operators is greater than another if there is a completely positive trace nonincreasing map under which the image of the first tuple satisfies certain semidefinite constraints relative to the other one. This preorder characterizes error probabilities in the case of testing a composite null hypothesis against a simple alternative hypothesis, as well as certain error probabilities in state discrimination. We present a sufficient condition for the existence of catalytic transformations between boxes, and a characterization of an associated asymptotic preorder, both expressed in terms of sandwiched Rényi divergences. This characterization of the asymptotic preorder directly shows that the strong converse exponent for a composite null hypothesis is equal to the maximum of the corresponding exponents for the pairwise simple hypothesis testing tasks.

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Gait recognition based on wavelet features with spectral regression kernel discriminant analysis

2017 40th International Conference on Telecommunications and Signal Processing (TSP) ◽

10.1109/tsp.2017.8076096 ◽

2017 ◽

Cited By ~ 2

Author(s):

Ait O. Lishani ◽

Larbi Boubchir ◽

Emad Khalifa ◽

Ahmed Bouridane

Keyword(s):

Discriminant Analysis ◽

Gait Recognition ◽

Kernel Discriminant Analysis ◽

Wavelet Features ◽

Spectral Regression

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Ultraviolet Spectroscopy Used to Fingerprint Five Wild-Grown Edible Mushrooms (Boletaceae) Collected from Yunnan, China

Journal of Spectroscopy ◽

10.1155/2016/7813405 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Yan Li ◽

Ji Zhang ◽

Tao Li ◽

Tianwei Yang ◽

Yuanzhong Wang ◽

...

Keyword(s):

Cluster Analysis ◽

Discriminant Analysis ◽

Hierarchical Cluster Analysis ◽

Visual Discrimination ◽

Uv Spectroscopy ◽

Hierarchical Cluster ◽

Uv Spectra ◽

Edible Mushrooms ◽

Ultraviolet Spectroscopy

Nowadays, wild-grown edible mushrooms which are natural, nutritious, and healthy get more and more popular by large consumers. In this paper, UV spectra of different Boletaceae mushrooms with the aid of partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were shown to be a practical and rapid method for discrimination purpose. The specimens of Boletus edulis, Boletus ferrugineus, Boletus tomentipes, Leccinum rugosiceps, and Xerocomus sp. were described based on the UV spectra. From the results, all the specimens were characterized by strong absorption at the wavelengths of 274 and 284 nm and showed the shoulder at 296 nm. However, changes could be seen in the peak heights at the same wavelength for different samples. After analyzing by chemometrics, visual discrimination among samples was presented and the relationships among them were also obtained. This study showed that UV spectroscopy combined with chemometrics methods could be used successfully as a simple and effective approach for characterization of these five wild-grown edible mushrooms at species and genus levels. Meanwhile, this rapid and simple methodology could also provide reference for the discrimination of edible mushrooms.

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