In this paper, we describe a novel semisupervised method for face classification using a low-rank subspace embedding. We demonstrate our approach through ...
Abstract—In this paper, we describe a novel semisupervised method for face classification using a low-rank subspace embed- ding. We demonstrate our approach ...
In this paper, we describe a novel semisupervised method for face classification using a low-rank subspace embedding. We demonstrate our approach through ...
A semisupervised deformed kernel function, using low-rank representation with consideration of local geometrical structure of data, is presented for the ...
May 4, 2024 · We first propose a novel semi-supervised SNMF model by seeking low-rank representation for the tensor synthesized by the pairwise constraint matrix and a ...
We propose a novel unsupervised feature extraction method called low-rank adaptive graph embedding (LRAGE), which can perform subspace learning and adaptive ...
In literatures, three label-embedding strategies [1] are usually adopted to identify label correlations. The first is derived from side information [4] such as ...
Jul 22, 2016 · We employ low rankness representation to construct a weighted adjacent matrix between samples and incorporate this graph into a semi-supervised ...
Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods.
Missing: classification. | Show results with:classification.
In this paper, we propose a new low rank learning method which constructs the low rank representation matrix utilizing label information to obtain a more ...