Clustering via Adaptive and Locality-constrained Graph Learning and Unsupervised ELM
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.03.045
Abstract: Abstract In this paper an effective graph learning method is proposed for clustering based on adaptive graph regularizations. Many graph learning methods focus on optimizing a global constraint on sparsity, low-rankness or weighted pair-wise distances,… read more here.
Keywords: elm; graph learning; locality; clustering via ... See more keywords
Single-Cell RNA-Sequencing Data Clustering via Locality Preserving Kernel Matrix Alignment
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DOI: 10.1109/access.2020.3036132
Abstract: Single-cell RNA-sequencing (scRNA-seq) data provide opportunities to reveal new insights into many biological problems such as elucidating cell types. An effective approach to elucidate cell types in complex tissues is to partition the cells into… read more here.
Keywords: cell rna; clustering via; cell; single cell ... See more keywords
Discriminative Projected Clustering via Unsupervised LDA.
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DOI: 10.1109/tnnls.2022.3202719
Abstract: This work focuses on the projected clustering problem. Specifically, an efficient and parameter-free clustering model, named discriminative projected clustering (DPC), is proposed for simultaneously low-dimensional and discriminative projection learning and clustering, from the perspective of… read more here.
Keywords: via unsupervised; projected clustering; unsupervised lda; dpc ... See more keywords