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- W4300483975 abstract "Hyper spectral imaging is a remote sensing technology, providing variety of applications such as material identification, space object identification, planetary exploitation etc. It deals with capturing continuum of images of the earth surface from different angles. Due to the multidimensional nature of the image, multi-way arrays are one of the possible solutions for analyzing hyper spectral data. This multi-way array is called tensor. Our approach deals with implementing three decomposition models LMLRA, BTD and CPD to the sample data for choosing the best decomposition of the data set. The results have proved that Block Term Decomposition (BTD) is the best tensor model for decomposing the hyper spectral image in to resultant factor matrices." @default.
- W4300483975 created "2022-10-03" @default.
- W4300483975 creator A5022734062 @default.
- W4300483975 creator A5031526464 @default.
- W4300483975 date "2015-03-23" @default.
- W4300483975 modified "2023-09-26" @default.
- W4300483975 title "A Comparative Analysis of Tensor Decomposition Models Using Hyper Spectral Image" @default.
- W4300483975 doi "https://doi.org/10.48550/arxiv.1503.06561" @default.
- W4300483975 hasPublicationYear "2015" @default.
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