An Algorithm with Projection Pursuit for Sliced Inverse Regression Model

2000 ◽  
pp. 255-260 ◽  
Author(s):  
Masahiro Mizuta ◽  
Hiroyuki Minami
Keyword(s):  
Regression Model ◽  
Projection Pursuit ◽  
Sliced Inverse Regression ◽  
Inverse Regression

Simultaneous estimations of optimal directions and optimal transformations for functional data

2018 ◽  
Vol 16 (05) ◽  
pp. 1850043
Author(s):  
Heng Chen ◽  
Wei Huang ◽  
Di-Rong Chen
Keyword(s):  
Hilbert Spaces ◽  
Functional Data ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Projection Pursuit ◽  
Sliced Inverse Regression ◽  
Inverse Regression ◽  
Powerful Method ◽  
Kernel Hilbert Spaces ◽  
Space Approach

Sliced inverse regression (SIR) is a powerful method to deal with a dimension reduction model. As well known, SIR is equivalent to a transformation-based projection pursuit, where the optimal directions are just the directions in SIR. In this paper, we consider simultaneous estimations of optimal directions for functional data and optimal transformations. We take a reproducing kernel Hilbert space approach. Both the directions and the transformations are chosen from reproducing kernel Hilbert spaces. A learning rate is established for the estimators.

scholarly journals A Sliced Inverse Regression (SIR) Decoding the Forelimb Movement from Neuronal Spikes in the Rat Motor Cortex

2016 ◽  
Vol 10 ◽  
Author(s):  
Shih-Hung Yang ◽  
You-Yin Chen ◽  
Sheng-Huang Lin ◽  
Lun-De Liao ◽  
Henry Horng-Shing Lu ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Sliced Inverse Regression ◽  
Inverse Regression ◽  
Forelimb Movement ◽  
Neuronal Spikes
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