Nov 17, 2018 · Low-rank representation (LRR) is one of the state-of-the-art methods, has been extensively employed in the existing graph-based learning models.
Graph-based approaches have been successfully used in semi-supervised learning (SSL) by weighting the affinity between the corresponding pairs of samples.
In this paper, we propose a symmetric LRR with adaptive distance penalty (SLRRADP) method for the small sample size (SSS) recognition problem. The graph ...
This paper proposes a new semi-supervised learning classification algorithm termed adaptive distance penalty non-negative low-rank representation (ADP-NNLRR).
This paper proposes a new semi-supervised learning classification algorithm termed adaptive distance penalty non-negative low-rank representation ...
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Adaptive distance penalty based nonnegative low-rank representation for semi-supervised learning ; Journal: Applied Intelligence, 2022, № 2, p. 1405-1416.
Mar 21, 2023 · This paper proposes a self-training subspace clustering algorithm based on adaptive confidence for gene expression data (SSCAC).
Adaptive distance penalty based nonnegative low-rank representation for semi-supervised learning · Computer Science. Applied Intelligence · 2022.
Dec 1, 2018 · Software defect prediction based on machine learning is an active research topic in the field of software engineering.
Jan 22, 2020 · In this paper, we propose a novel integrated framework for cancer clustering known as the non-negative symmetric low-rank representation with ...