All Relations between semantics and matrix compartment

Publication Sentence Publish Date Extraction Date Species
Shiqiang Yang, Guohao Fan, Lele Bai, Cheng Zhao, Dexin L. SGC-VSLAM: A Semantic and Geometric Constraints VSLAM for Dynamic Indoor Environments. Sensors (Basel, Switzerland). vol 20. issue 8. 2020-05-23. PMID:32344724. the semantic bounding box generated by yolo v3 (you only look once, v3) was used to calculate a more accurate fundamental matrix between adjacent frames, which was then used to filter all of the truly dynamic features. 2020-05-23 2023-08-13 Not clear
Morgan C Moyer, Kristen Syret. The semantics of questions. Wiley interdisciplinary reviews. Cognitive science. vol 10. issue 6. 2020-03-02. PMID:31348617. when questions are embedded under a matrix verb like "know," which takes the question as a sentential complement, how does the semantics of questions feed into the assessment of the proposition expressed by this declarative utterance? 2020-03-02 2023-08-13 human
Jianghong Ma, Tommy W S Cho. Label-specific feature selection and two-level label recovery for multi-label classification with missing labels. Neural networks : the official journal of the International Neural Network Society. vol 118. 2019-12-06. PMID:31254766. an instance-wise semantic relational graph and a label-wise semantic relational graph are used in this mechanism to recover the label matrix. 2019-12-06 2023-08-13 Not clear
Xiaoqiang Lu, Xiangtao Zheng, Xuelong L. Latent Semantic Minimal Hashing for Image Retrieval. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 26. issue 1. 2019-11-20. PMID:27849528. the latent semantic feature is learned based on matrix decomposition to refine original feature, thereby it makes the learned feature more discriminative. 2019-11-20 2023-08-13 Not clear
Lei Zhu, Jialie Shen, Liang Xie, Zhiyong Chen. Unsupervised Topic Hypergraph Hashing for Efficient Mobile Image Retrieval. IEEE transactions on cybernetics. vol 47. issue 11. 2019-11-20. PMID:28113794. in our method, relations between images and semantic topics are first discovered via robust collective non-negative matrix factorization. 2019-11-20 2023-08-13 Not clear
Yu Kong, Yun F. Discriminative Relational Representation Learning for RGB-D Action Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 25. issue 6. 2019-11-20. PMID:28113902. our method factorizes the feature matrix of each modality, and enforces the same semantics for them in order to learn shared features from multimodal data. 2019-11-20 2023-08-13 human
Maoguo Gong, Xiangming Jiang, Hao Li, Kay Chen Ta. Multiobjective Sparse Non-Negative Matrix Factorization. IEEE transactions on cybernetics. vol 49. issue 8. 2019-11-20. PMID:29994343. non-negative matrix factorization (nmf) is becoming increasingly popular in many research fields due to its particular properties of semantic interpretability and part-based representation. 2019-11-20 2023-08-13 Not clear
Ying-Ying Zhang, Shuo Zhang, Ping Zhang, Hai-Zhen Song, Xin-Gang Zhan. Local Regression Ranking for Saliency Detection. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-10-03. PMID:31567087. low-level image features as well as high-level semantic information extracted from deep neural networks are used for the laplacian matrix learning. 2019-10-03 2023-08-13 Not clear
Di Wang, Xinbo Gao, Xiumei Wang, Lihuo H. Label Consistent Matrix Factorization Hashing for Large-Scale Cross-Modal Similarity Search. IEEE transactions on pattern analysis and machine intelligence. vol 41. issue 10. 2019-09-23. PMID:30059294. when semantic labels of training data are given, the algorithms often transform the labels into pairwise similarities, which gives rise to the following problems: (1) constructing pairwise similarity matrix requires enormous storage space and a large amount of calculation, making these methods unscalable to large-scale data sets; (2) transforming labels into pairwise similarities loses the category information of the training data. 2019-09-23 2023-08-13 Not clear
Di Wang, Xinbo Gao, Xiumei Wang, Lihuo H. Label Consistent Matrix Factorization Hashing for Large-Scale Cross-Modal Similarity Search. IEEE transactions on pattern analysis and machine intelligence. vol 41. issue 10. 2019-09-23. PMID:30059294. to address these challenges, this paper introduces a simple yet effective supervised multimodal hashing method, called label consistent matrix factorization hashing (lcmfh), which focuses on directly utilizing semantic labels to guide the hashing learning procedure. 2019-09-23 2023-08-13 Not clear
Jianghong Ma, Tommy W S Cho. Topic-Based Algorithm for Multilabel Learning With Missing Labels. IEEE transactions on neural networks and learning systems. vol 30. issue 7. 2019-07-23. PMID:30442616. the proposed algorithm can recover the label matrix according to local, topic-wise, and global semantic properties. 2019-07-23 2023-08-13 Not clear
Cheng Deng, Erkun Yang, Tongliang Liu, Jie Li, Wei Liu, Dacheng Ta. Unsupervised Semantic-Preserving Adversarial Hashing for Image Search. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 8. 2019-06-24. PMID:30872226. integrating the supervision of this semantic similarity matrix into the adversarial learning framework can efficiently preserve the semantic information of training data in hamming space. 2019-06-24 2023-08-13 Not clear
Jan Ketil Arnulf, Kai Rune Larsen, Øyvind Lund Martinsen, Thore Egelan. The failing measurement of attitudes: How semantic determinants of individual survey responses come to replace measures of attitude strength. Behavior research methods. vol 50. issue 6. 2019-04-30. PMID:29330764. exploring the degree to which individual responses are influenced by semantics, we hypothesized that in many cases, information about attitude strength is actually filtered out as noise in the commonly used correlation matrix. 2019-04-30 2023-08-13 human
Graham Pluc. Preliminary Validation of a Free-to-Use, Brief Assessment of Adult Intelligence for Research Purposes: The Matrix Matching Test. Psychological reports. vol 122. issue 2. 2019-04-24. PMID:29540106. a total sample of 176 adult participants, from various settings, was assessed with a set of matrix tasks that involved either visuospatial (fluid) or semantic (crystallized) reasoning. 2019-04-24 2023-08-13 human
José Fonseca, Ana Raposo, Isabel Pavão Martin. Cognitive performance and aphasia recovery. Topics in stroke rehabilitation. vol 25. issue 2. 2018-10-16. PMID:29072540. assessment comprised non-verbal tests of attention/processing speed (symbol search, cancelation task), executive functioning (matrix reasoning, tower of hanoi, clock drawing, motor initiative), semantic (camel and cactus test), episodic and immediate memory (memory for faces test, 5 objects memory test, and spatial span. 2018-10-16 2023-08-13 human
Zechao Li, Jinhui Tan. Weakly Supervised Deep Matrix Factorization for Social Image Understanding. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 26. issue 1. 2018-07-30. PMID:27831878. different from previous work, we propose a novel weakly supervised deep matrix factorization algorithm, which uncovers the latent image representations and tag representations embedded in the latent subspace by collaboratively exploring the weakly supervised tagging information, the visual structure, and the semantic structure. 2018-07-30 2023-08-13 Not clear
Alan M Race, Andrew D Palmer, Alex Dexter, Rory T Steven, Iain B Styles, Josephine Bunc. SpectralAnalysis: Software for the Masses. Analytical chemistry. vol 88. issue 19. 2018-07-26. PMID:27558772. here, we present software that can be used through the entire analysis workflow, from raw data through preprocessing (including a wide range of methods for smoothing, baseline correction, normalization, and image generation) to multivariate analysis (for example, memory efficient principal component analysis (pca), non-negative matrix factorization (nmf), maximum autocorrelation factor (maf), and probabilistic latent semantic analysis (plsa)), for data sets acquired from single experiments to large multi-instrument, multimodality, and multicenter studies. 2018-07-26 2023-08-13 Not clear
Fuzhen Zhuang, Xuebing Li, Xin Jin, Dapeng Zhang, Lirong Qiu, Qing H. Semantic Feature Learning for Heterogeneous Multitask Classification via Non-Negative Matrix Factorization. IEEE transactions on cybernetics. vol 48. issue 8. 2018-07-23. PMID:28792910. semantic feature learning for heterogeneous multitask classification via non-negative matrix factorization. 2018-07-23 2023-08-13 Not clear
Fuzhen Zhuang, Xuebing Li, Xin Jin, Dapeng Zhang, Lirong Qiu, Qing H. Semantic Feature Learning for Heterogeneous Multitask Classification via Non-Negative Matrix Factorization. IEEE transactions on cybernetics. vol 48. issue 8. 2018-07-23. PMID:28792910. we then propose a non-negative matrix factorization-based multitask method (mtnmf) to learn a common semantic feature space underlying different heterogeneous feature spaces of each task. 2018-07-23 2023-08-13 Not clear
Adam E Green, Katherine A Spiegel, Evan J Giangrande, Adam B Weinberger, Natalie M Gallagher, Peter E Turkeltau. Thinking Cap Plus Thinking Zap: tDCS of Frontopolar Cortex Improves Creative Analogical Reasoning and Facilitates Conscious Augmentation of State Creativity in Verb Generation. Cerebral cortex (New York, N.Y. : 1991). vol 27. issue 4. 2017-04-27. PMID:27075035. in a novel analogy finding task, participants under tdcs formulated substantially more creative analogical connections in a large matrix search space (creativity indexed via latent semantic analysis). 2017-04-27 2023-08-13 human