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In this paper, we propose a new semi-supervised concept factorization method, called Constrained Concept Factorization with Graph Laplacian (CCF-GL), which not ...
In this paper, we propose a new semi-supervised concept factorization method, called Constrained Concept Factorization with Graph Laplacian (CCF-GL), which not ...
Constrained concept factorization with graph Laplacian | Semantic ...
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This paper proposes a new semi-supervised concept factorization method, called Constrained Concept Factorization with Graph Laplacian (CCF-GL), ...
A rank constraint is imposed on the Laplacian matrix of the learned graph resulting in the existence of exactly k connected components. Then it is used to ...
To achieve such ideal clustering structures, we impose a rank constraint on the Laplacian graph of the new data similar- ity matrix, thereby guaranteeing the ...
Missing: concept | Show results with:concept
In this paper, the goal is to learn a sparse graph under the Laplacian constrained. GGM, where the precision matrix obeys Laplacian structural constraints. The ...
The problem of estimating the graph structure that fits a collection of data can be framed as Laplacian. Constrained Gaussian Graphical Model (LCGGM) inference.
Missing: concept factorization
Yuan-Yuan Lu, Hong-Qi Li, Hong-Bin Yu, Pan Chen: Constrained concept factorization with graph Laplacian. ICMLC 2016: 362-368.
Apr 29, 2024 · We propose the Cauchy hyper-graph Laplacian non-negative matrix factorization (CHLNMF) as a unique approach to address these issues.
Thus, we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to smooth the document-to-concept mapping, LCCF can extract ...