Results 141 to 150 of about 13,849 (192)

Persistent Mayer Dirac. [PDF]

J Phys Complex
Suwayyid F, Wei GW.
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Global understanding via local extraction for data clustering and visualization. [PDF]

Patterns (N Y)
Zhang Z, Li B.
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GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. [PDF]

PLoS One
Guo B, Deng L, Lian T.
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A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences. [PDF]

J Chem Inf Model
Wee J, Jiang J.
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Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection. [PDF]

Bioinformatics
Liu E, Lyu H, Liu Y, Fu L, Cheng X, Yin X.   +5 more
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High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model

Schmutz V, Haydaroglu A, Wang S, Feng Y, Carandini M, Harris KD.   +5 more
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Block-Diagonal Guided Symmetric Nonnegative Matrix Factorization

IEEE Transactions on Knowledge and Data Engineering, 2021
Symmetric nonnegative matrix factorization (SNMF) is effective to cluster nonlinearly separable data, which uses the constructed graph to capture the structure of inherent clusters. Nevertheless, many SNMF-based clustering approaches implicitly enforce either the sparseness constraint or the smoothness constraint with the limited supervised information
Yalan Qin, Guorui Feng, Yanli Ren, Xinpeng Zhang   +3 more
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Structured subspace learning-induced symmetric nonnegative matrix factorization

Signal Processing, 2021
Abstract Symmetric NMF (SNMF) is able to determine the inherent cluster structure with the constructed graph. However, the mapping between the empirically constructed similarity representation and the desired one may contain complex structural and hierarchical information, which is not easy to capture with single learning approaches. Then, we propose
Yalan Qin, Hanzhou Wu, Guorui Feng
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Pairwise Constraint Propagation-Induced Symmetric Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems, 2018
As a variant of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) has shown to be effective for capturing the cluster structure embedded in the graph representation. In contrast to the existing SNMF-based clustering methods that empirically construct the similarity matrix and rigidly introduce the supervisory information to the assignment ...
Wenhui Wu, Yuheng Jia, Sam Kwong, Junhui Hou   +3 more
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Symmetric Nonnegative Matrix Factorization With Beta-Divergences

IEEE Signal Processing Letters, 2012
Nonnegative matrix factorization/approximation (NMF) is a recently developed technology for dimensionality reduction and parts based data representation. The symmetric NMF (SNMF) decomposition is a special case of NMF, in which both factors are identical. This paper discusses SNMF decomposition with beta divergences.
Min Shi, Qingming Yi, Jun Lv
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