Jun 5, 2018 · This paper presents a parametric low-dimensional (LD) representation learning method that allows to reconstruct high-dimensional (HD) input ...
Dec 19, 2018 · Abstract—This paper presents a parametric low-dimensional. (LD) representation learning method that allows to reconstruct.
The proposed parametric low-dimensional representation learning method can be used as an alternative approach to compressive sensing (CS); however, ...
This paper presents a parametric low-dimensional (LD) representation learning method that allows to reconstruct high-dimensional (HD) input vectors in an ...
Bibliographic details on Reconstructible Nonlinear Dimensionality Reduction via Joint Dictionary Learning.
This paper proposes an invertible nonlinear dimensionality reduction method via jointly learning dictionaries in both the original high dimensional data ...
Abstract: This paper proposes an invertible nonlinear dimensionality reduction method via jointly learning dictionaries in both the original high dimensional ...
A novel joint dimension reduction and dictionary learning framework is proposed in this paper for high-dimensional data classification.
Missing: Reconstructible | Show results with:Reconstructible
Non-parametric kernel learning (NPKL) methods have been attracted much more attention and achieved outstanding classification performance in the past few years.
Reconstructible nonlinear dimensionality reduction via joint dictionary learning. X Wei, H Shen, Y Li, X Tang, F Wang, M Kleinsteuber, YL Murphey. IEEE ...