TY - JOUR
T1 - Blessing of randomness against the curse of dimensionality
AU - Kucheryavskiy, Sergey V.
N1 - This article has been found as a 'Free Version' from the Publisher on September 4th 2018. When the access to the article closes, please notify vbn@aub.aau.dk
PY - 2018/1
Y1 - 2018/1
N2 - Modern hyperspectral images, especially acquired in remote sensing and from on‐field measurements, can easily contain from hundreds of thousands to several millions of pixels. This often leads to a quite long computational time when, eg, the images are decomposed by Principal Component Analysis (PCA) or similar algorithms. In this paper, we are going to show how random- ization can tackle this problem. The main idea is described in detail by Halko et al in 2011 and can be used for speeding up most of the low‐rank matrix decomposition methods. The paper explains this approach using visual interpretation of its main steps and shows how the use of randomness influences the speed and accuracy of PCA decomposition of hyperspectral images.
AB - Modern hyperspectral images, especially acquired in remote sensing and from on‐field measurements, can easily contain from hundreds of thousands to several millions of pixels. This often leads to a quite long computational time when, eg, the images are decomposed by Principal Component Analysis (PCA) or similar algorithms. In this paper, we are going to show how random- ization can tackle this problem. The main idea is described in detail by Halko et al in 2011 and can be used for speeding up most of the low‐rank matrix decomposition methods. The paper explains this approach using visual interpretation of its main steps and shows how the use of randomness influences the speed and accuracy of PCA decomposition of hyperspectral images.
KW - hyperspectral images
KW - matrix decomposition
KW - principal component analysis
KW - probabilistic algorithms
KW - randomisation
UR - https://onlinelibrary.wiley.com/doi/pdf/10.1002/cem.2966
U2 - 10.1002/cem.2966
DO - 10.1002/cem.2966
M3 - Journal article
SN - 0886-9383
VL - 32
SP - 1
EP - 14
JO - Journal of Chemometrics
JF - Journal of Chemometrics
IS - 1
M1 - e2966
ER -