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In this paper, we propose a discriminant sparse coding approach with geometrical constraint. Labels are used to learn an intrinsic graph and a penalty graph, ...
Feb 22, 2023 · Abstract. The classical sparse coding model represents visual stimuli as a linear combination of a handful of learned basis functions that ...
He et al. [11] proposed non-negative sparse coding for discriminative semi-supervised learning. Zhang et al. [12] proposed to use non-negative sparse coding, ...
The results show that preserving the sparse representation of the signals from the original space in the (lower) dimensional projected space is beneficial ...
(PDF) Discriminative sparse neighbor coding - ResearchGate
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To address this shortcoming, we investigate how to develop new sparse representations which preserve feature similarities. We commence by establishing two ...
Missing: Discriminant | Show results with:Discriminant
The choice of the over-complete dictionary that sparsely represents data is of prime importance for sparse coding- based image super-resolution.
Missing: Discriminant | Show results with:Discriminant
We pursue a hierarchical sparse cod- ing approach that learns features useful in discriminating images across locations, by initializing it with a geometric.
Missing: Discriminant | Show results with:Discriminant
In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar ...
To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks ...
Missing: Discriminant | Show results with:Discriminant
With its ℓ0 pseudo-norm constraint, sparse coding is an NP-hard problem whose ... Fishers discriminant to the sparse coding ... includes geometric information as ...