| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Aiping Huang, Weiling Chen, Tiesong Zhao, Chang Wen Che. Joint Learning of Latent Similarity and Local Embedding for Multi-View Clustering. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 30. 2021-08-02. PMID:34310300. |
moreover, an effective low-dimensional representation is obtained by applying local embedding on the similarity matrix, which preserves the local intrinsic structure of data through dimensionality reduction. |
2021-08-02 |
2023-08-13 |
Not clear |
| Aiping Huang, Weiling Chen, Tiesong Zhao, Chang Wen Che. Joint Learning of Latent Similarity and Local Embedding for Multi-View Clustering. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 30. 2021-08-02. PMID:34310300. |
the solution, which consists of a similarity matrix and a low-dimensional representation, is ultimately integrated with spectral clustering or k-means for multi-view clustering. |
2021-08-02 |
2023-08-13 |
Not clear |
| Wei Zhang, Xiaoli Xue, Xiaoying Zheng, Zizhu Fa. NMFLRR: Clustering scRNA-seq data by integrating non-negative matrix factorization with low rank representation. IEEE journal of biomedical and health informatics. vol PP. 2021-07-26. PMID:34310328. |
nmflrr: clustering scrna-seq data by integrating non-negative matrix factorization with low rank representation. |
2021-07-26 |
2023-08-13 |
Not clear |
| Wei Zhang, Xiaoli Xue, Xiaoying Zheng, Zizhu Fa. NMFLRR: Clustering scRNA-seq data by integrating non-negative matrix factorization with low rank representation. IEEE journal of biomedical and health informatics. vol PP. 2021-07-26. PMID:34310328. |
we present a new computational framework to identify cell types by integrating low-rank representation (lrr) and nonnegative matrix factorization (nmf); this framework is named nmflrr. |
2021-07-26 |
2023-08-13 |
Not clear |
| Simon Wiedemann, Klaus-Robert Muller, Wojciech Same. Compact and Computationally Efficient Representation of Deep Neural Networks. IEEE transactions on neural networks and learning systems. vol 31. issue 3. 2021-07-12. PMID:31150347. |
in order to efficiently exploit such matrices, one usually relies on, inter alia, sparse matrix representations. |
2021-07-12 |
2023-08-13 |
Not clear |
| Ya-Li Zhu, Sha-Sha Yuan, Jin-Xing Li. Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization for Single-Cell RNA-seq Analysis. Interdisciplinary sciences, computational life sciences. 2021-07-07. PMID:34231183. |
in this paper, we propose a novel clustering method similarity and dissimilarity regularized nonnegative matrix factorization (sdcnmf) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. |
2021-07-07 |
2023-08-13 |
Not clear |
| Ya-Li Zhu, Sha-Sha Yuan, Jin-Xing Li. Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization for Single-Cell RNA-seq Analysis. Interdisciplinary sciences, computational life sciences. 2021-07-07. PMID:34231183. |
this paper proposes a novel clustering method similarity and dissimilarity regularized nonnegative matrix factorization (sdcnmf) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. |
2021-07-07 |
2023-08-13 |
Not clear |
| Quang-Thai Ho, Trinh-Trung-Duong Nguyen, Nguyen Quoc Khanh Le, Yu-Yen O. FAD-BERT: Improved prediction of FAD binding sites using pre-training of deep bidirectional transformers. Computers in biology and medicine. vol 131. 2021-07-02. PMID:33601085. |
thereby, we propose a new approach based on pre-training of bidirectional encoder representations from transformers (bert), position-specific scoring matrix profiles (pssm), amino acid index database (aaindex) to predict fad-binding sites from the transport proteins which are found in nature recently. |
2021-07-02 |
2023-08-13 |
Not clear |
| Ye-Sen Sun, Le Ou-Yang, Dao-Qing Da. LRSK: a low-rank self-representation K-means method for clustering single-cell RNA-sequencing data. Molecular omics. vol 16. issue 5. 2021-07-01. PMID:32572422. |
in this paper, we propose a novel adaptive joint clustering framework, named the low-rank self-representation k-means method (lrsk), to learn the data representation matrix and cluster indicator matrix jointly from scrna-seq data. |
2021-07-01 |
2023-08-13 |
Not clear |
| Jie Chen, Shengxiang Yang, Hua Mao, Conor Fah. Multiview Subspace Clustering Using Low-Rank Representation. IEEE transactions on cybernetics. vol PP. 2021-07-01. PMID:34185655. |
most previous studies have performed the task of learning multiview representations by individually constructing an affinity matrix for each view without simultaneously exploiting the intrinsic characteristics of multiview data. |
2021-07-01 |
2023-08-13 |
Not clear |
| Jie Chen, Shengxiang Yang, Hua Mao, Conor Fah. Multiview Subspace Clustering Using Low-Rank Representation. IEEE transactions on cybernetics. vol PP. 2021-07-01. PMID:34185655. |
mlrr considers symmetric low-rank representations (lrrs) to be an approximately linear spatial transformation under the new base, that is, the multiview data themselves, to fully exploit the angular information of the principal directions of lrrs, which is adopted to construct an affinity matrix for multiview subspace clustering, under a symmetric condition. |
2021-07-01 |
2023-08-13 |
Not clear |
| Jianhua Xu, Zhi-Hong Ma. Multilabel Feature Extraction Algorithm via Maximizing Approximated and Symmetrized Normalized Cross-Covariance Operator. IEEE transactions on cybernetics. vol 51. issue 7. 2021-06-28. PMID:31056530. |
in this paper, we approximate this measure using moore-penrose inverse matrix, linear kernel for feature space, and delta kernel for label space, and then symmetrize the entire matrix in the trace operation, resulting in an effective approximated and symmetrized representation. |
2021-06-28 |
2023-08-13 |
Not clear |
| Paolo Facchi, Giovanni Gramegna, Arturo Kondera. Entropy of Quantum States. Entropy (Basel, Switzerland). vol 23. issue 6. 2021-06-26. PMID:34064265. |
moreover, it can be shown to be equal to the von neumann entropy of the unique representative density matrix belonging to the operator algebra of a multiplicity-free hilbert-space representation. |
2021-06-26 |
2023-08-13 |
human |
| Zhen Zhang, Jiajun Bu, Zhao Li, Chengwei Yao, Can Wang, Jia W. TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks. Neural networks : the official journal of the International Neural Network Society. vol 140. 2021-06-25. PMID:33743320. |
specifically, we devise two coupled memory networks to store and update node embeddings in the external matrices explicitly and dynamically, which forms deep matrix representations and thus could enhance the expressiveness of the node embeddings. |
2021-06-25 |
2023-08-13 |
Not clear |
| Li Xiao, Junqi Wang, Peyman H Kassani, Yipu Zhang, Yuntong Bai, Julia M Stephen, Tony W Wilson, Vince D Calhoun, Yu-Ping Wan. Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data. IEEE transactions on medical imaging. vol 39. issue 5. 2021-06-24. PMID:31796393. |
specifically, we first generate hyperedges from fmri time series based on sparse representation, then employ hypergraph learning to adaptively learn hyperedge weights, and finally define a hypergraph similarity matrix to represent the fcn. |
2021-06-24 |
2023-08-13 |
human |
| Weisheng Dong, Chen Zhou, Fangfang Wu, Jinjian Wu, Guangming Shi, Xin L. Model-Guided Deep Hyperspectral Image Super-Resolution. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 30. 2021-06-24. PMID:33979283. |
the representation of the observation matrix by subnetworks also allows the unfolded deep hsisr network to work with different hsi situations, which enhances the flexibility of mog-dcn. |
2021-06-24 |
2023-08-13 |
Not clear |
| Haoran Yu, Paul A Dalb. A beginner's guide to molecular dynamics simulations and the identification of cross-correlation networks for enzyme engineering. Methods in enzymology. vol 643. 2021-06-23. PMID:32896280. |
a matrix of all atom-wise cross-correlation coefficients is finally obtained, which can be displayed in a graphical representation termed a dynamical cross-correlation matrix. |
2021-06-23 |
2023-08-13 |
Not clear |
| Yanbei Liu, Lianxi Fan, Changqing Zhang, Tao Zhou, Zhitao Xiao, Lei Geng, Dinggang She. Incomplete multi-modal representation learning for Alzheimer's disease diagnosis. Medical image analysis. vol 69. 2021-06-23. PMID:33460880. |
then, the latent representations measuring statistical dependence learned from the complete view are used to complement the kernel matrix of the incomplete view in the kernel space. |
2021-06-23 |
2023-08-13 |
Not clear |
| Yanbei Liu, Lianxi Fan, Changqing Zhang, Tao Zhou, Zhitao Xiao, Lei Geng, Dinggang She. Incomplete multi-modal representation learning for Alzheimer's disease diagnosis. Medical image analysis. vol 69. 2021-06-23. PMID:33460880. |
finally, a kernel based multi-view method is applied to the learned kernel matrix for the acquisition of common representations. |
2021-06-23 |
2023-08-13 |
Not clear |
| Tao Zhao, Arthur Zwaenepoel, Jia-Yu Xue, Shu-Min Kao, Zhen Li, M Eric Schranz, Yves Van de Pee. Whole-genome microsynteny-based phylogeny of angiosperms. Nature communications. vol 12. issue 1. 2021-06-22. PMID:34108452. |
here, by combining synteny network analysis, matrix representation, and maximum likelihood phylogenetic inference, we provide a way to reconstruct phylogenies based on microsynteny information. |
2021-06-22 |
2023-08-13 |
Not clear |