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Extension of Sample Dimension and Sparse Representation Based Classification of Low-Dimension Data
2019As we know, sparse representation methods can achieve high accuracy for classification of high-dimensional data. However, they usually show poor performance in performing classification of low-dimensional data. In this paper, the increase of the sample dimension for sparse representation is studied and surprising accuracy improvement is obtained.
Qian Wang +3 more
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The Effect of Sub-sampling on Hyperspectral Dimension Reduction
2013Hyperspectral images which are captured in narrow bands in continuous manner contain very large data. This data need high processing power to classify and may contain redundant information. A variety of dimension reduction methods are used to cope with this high dimensionality.
Ali Ömer Kozal +2 more
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Consideration of sample dimension for ultrasonic levitation
IEEE Symposium on Ultrasonics, 2002To study ultrasonic levitation, a high-intensity ultrasound field was generated in air by a new type of sound source using a stepped circular vibrating plate, and the sound pressure level was obtained up to 168 dB at a frequency of 20 kHz. This sound source was equipped with a reflecting plate.
T. Otsuka, K. Higuchi, K. Seya
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Sampling spaces and arithmetic dimension
2011This paper introduces the twin concepts of sampling spaces and arithmetic dimension, which together address the question of how to count the number, or measure the size of, families of objects over a number field or global field. It can be seen as an alternative to coarse moduli schemes, with more attention to the arithmetic properties of the ambient ...
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Sampling and Interpolation in Two Dimensions
2003Sampling and interpolation in two dimensions is much richer than in one dimension. Not only are there polar coordinates and other coordinate systems in addition to cartesian, but sampling can be done along lines as well as at points. The distinction between point and line sampling will be discussed first.
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The Sampling Theorem in Higher Dimensions
1991The first generalization of the Shannon sampling theorem to two and more dimensions was done by Peterson and Middleton. Multidimensional signals include black and white images, which can be depicted as two dimensional functions with zero corresponding to black and one as white. Intermediate grey levels are classified between these limits.
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Fast Poisson disk sampling in arbitrary dimensions
ACM SIGGRAPH 2007 sketches, 2007In many applications in graphics, particularly rendering, generating samples from a blue noise distribution is important. However, existing efficient techniques do not easily generalize beyond two dimensions. Here I demonstrate a simple modification to dart throwing which permits generation of Poisson disk samples in O(N) time, easily implemented in ...
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On dimensioning of samples in testing hypotheses
Kybernetika, 2021Ferdinand Österreicher, Heinz Stadler
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