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Journal articles on the topic 'Multi-locality'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Ebbe, Helen, and Stefan Wolf. "Multi-User Non-Locality Amplification." IEEE Transactions on Information Theory 60, no. 2 (2014): 1159–67. http://dx.doi.org/10.1109/tit.2013.2292515.

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2

Qian, Jiangbo, Qiang Zhu, and Huahui Chen. "Multi-Granularity Locality-Sensitive Bloom Filter." IEEE Transactions on Computers 64, no. 12 (2015): 3500–3514. http://dx.doi.org/10.1109/tc.2015.2401011.

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3

McLoughlin, Seán. "Locating Muslim diasporas: multi-locality, multi-disciplinarity and performativity." Ethnic and Racial Studies 40, no. 3 (2017): 421–27. http://dx.doi.org/10.1080/01419870.2017.1249503.

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4

Lehtonen, Olli, Toivo Muilu, and Hilkka Vihinen. "Multi-Local Living – An Opportunity for Rural Health Services in Finland?" European Countryside 11, no. 2 (2019): 257–80. http://dx.doi.org/10.2478/euco-2019-0013.

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Abstract In the simplest definition, multi-local living means that a person or family have more than one residence or place to stay. In Finland, multi-locality has become a common phenomenon in recent decades, but the effects of it are not yet considered in decision-making or planning. This is because the “invisible population” created by multi-locality is not reflected in traditional population statistics. The assumption in this article is that multi-locality would provide opportunities to improve accessibility of health and social services in rural areas. The assumption is tested in the Nort

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5

Wood, Gerald, Nicola Hilti, Caroline Kramer, and Michaela Schier. "A Residential Perspective on Multi-Locality: Editorial." Tijdschrift voor economische en sociale geografie 106, no. 4 (2015): 363–77. http://dx.doi.org/10.1111/tesg.12158.

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6

Jing, Ling, Yi Li, and Hongjie Zhang. "Robust multi-view locality preserving regression embedding." PeerJ Computer Science 10 (December 20, 2024): e2619. https://doi.org/10.7717/peerj-cs.2619.

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Feature extraction research has witnessed significant advancements in recent decades, particularly with single-view graph embedding (GE) methods that demonstrate clear advantages by incorporating structural information. However, multi-view data includes descriptions from various perspectives or sensors, offering richer and more comprehensive information compared to single-view data. Research interest in multi-view feature extraction is steadily increasing. Hence, there is a pressing need for a comprehensive framework that extends single-view methods, especially effective GE methods, into multi

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7

Zhao, Yue, Xinge You, Shujian Yu, et al. "Multi-view manifold learning with locality alignment." Pattern Recognition 78 (June 2018): 154–66. http://dx.doi.org/10.1016/j.patcog.2018.01.012.

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8

Wu, Fei, Xiao-Yuan Jing, and Qinghua Huang. "Uncorrelated Locality-Sensitive Multi-view Discriminant Analysis." National Academy Science Letters 43, no. 4 (2020): 327–31. http://dx.doi.org/10.1007/s40009-019-00864-4.

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9

Long, Jiang, Qi Zhang, Xiaohuan Lu, Jie Wen, Lian Zhao, and Wulin Xie. "Multi-scale locality preserving projection for partial multi-view incomplete multi-label learning." Neural Networks 180 (December 2024): 106748. http://dx.doi.org/10.1016/j.neunet.2024.106748.

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10

ZHANG, Zhao, Ning YE, and Qiao-Lin YE. "Locality Preserving Multi-Projection Vector Fisher Discriminant Analysis." Chinese Journal of Computers 33, no. 5 (2010): 865–76. http://dx.doi.org/10.3724/sp.j.1016.2010.00865.

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11

Sheng, Xiang-Rong, De-Chuan Zhan, Su Lu, and Yuan Jiang. "Multi-View Anomaly Detection: Neighborhood in Locality Matters." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4894–901. http://dx.doi.org/10.1609/aaai.v33i01.33014894.

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Identifying anomalies in multi-view data is a difficult task due to the complicated data characteristics of anomalies. Specifically, there are two types of anomalies in multi-view data–anomalies that have inconsistent features across multiple views and anomalies that are consistently anomalous in each view. Existing multi-view anomaly detection approaches have some issues, e.g., they assume multiple views of a normal instance share consistent and normal clustering structures while anomaly exhibits anomalous clustering characteristics across multiple views. When there are no clusters in data, i

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12

Greinke, Lena, and Linda Lange. "Multi-locality in rural areas – an underestimated phenomenon." Regional Studies, Regional Science 9, no. 1 (2022): 67–81. http://dx.doi.org/10.1080/21681376.2021.2025417.

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13

Qian, Jiangbo, Zhipeng Huang, Qiang Zhu, and Huahui Chen. "Hamming Metric Multi-Granularity Locality-Sensitive Bloom Filter." IEEE/ACM Transactions on Networking 26, no. 4 (2018): 1660–73. http://dx.doi.org/10.1109/tnet.2018.2850536.

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14

Weichhart, Peter. "Residential Multi-Locality: In Search of Theoretical Frameworks." Tijdschrift voor economische en sociale geografie 106, no. 4 (2015): 378–91. http://dx.doi.org/10.1111/tesg.12156.

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15

Wilms, Matthias, Heinz Handels, and Jan Ehrhardt. "Multi-resolution multi-object statistical shape models based on the locality assumption." Medical Image Analysis 38 (May 2017): 17–29. http://dx.doi.org/10.1016/j.media.2017.02.003.

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16

Yang, Ying, Shu Xiao, and Huaixin Cao. "Nonlocality of a type of multi-star-shaped quantum networks." Journal of Physics A: Mathematical and Theoretical 55, no. 2 (2022): 025303. http://dx.doi.org/10.1088/1751-8121/ac3f89.

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Abstract The correlations in quantum networks have attracted strong interest due to the fact that linear Bell inequalities derived from one source are useless for characterizing multipartite correlations of general quantum networks. In this paper, a type of multi-star-shaped quantum networks are introduced and discussed. Such a network consists of three-grade nodes: the first grade is named party (node) A, the second one consists of m nodes marked B 1, B 2, …, B m , which are stars of A and the third one consists of m 2 nodes C k j ( j , k = 1 , 2 , … , m ) , where C k j ( k = 1 , 2 , … , m )

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17

Bertuzzo, Elisa T. "Chinese urbanism between multi-locality and inter-urban growth." disP - The Planning Review 51, no. 1 (2015): 104. http://dx.doi.org/10.1080/02513625.2015.1038087.

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18

Feng, Lin, Xiangzhu Meng, and Huibing Wang. "Multi-view Locality Low-rank Embedding for Dimension Reduction." Knowledge-Based Systems 191 (March 2020): 105172. http://dx.doi.org/10.1016/j.knosys.2019.105172.

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19

Steel, Griet, Torben Birch-Thomsen, Ine Cottyn, et al. "Multi-activity, Multi-locality and Small-Town Development in Cameroon, Ghana, Rwanda and Tanzania." European Journal of Development Research 31, no. 1 (2018): 12–33. http://dx.doi.org/10.1057/s41287-018-0183-y.

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20

GUO, Lihua. "Locality-Constrained Multi-Task Joint Sparse Representation for Image Classification." IEICE Transactions on Information and Systems E96.D, no. 9 (2013): 2177–81. http://dx.doi.org/10.1587/transinf.e96.d.2177.

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21

Kwon, Woo-Cheol, Tushar Krishna, and Li-Shiuan Peh. "Locality-oblivious cache organization leveraging single-cycle multi-hop NoCs." ACM SIGPLAN Notices 49, no. 4 (2014): 715–28. http://dx.doi.org/10.1145/2644865.2541976.

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22

Kwon, Woo-Cheol, Tushar Krishna, and Li-Shiuan Peh. "Locality-oblivious cache organization leveraging single-cycle multi-hop NoCs." ACM SIGARCH Computer Architecture News 42, no. 1 (2014): 715–28. http://dx.doi.org/10.1145/2654822.2541976.

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23

Schmidt, Leigh Anne, and Stephanie Buechler. "Honduran boys confronting adversity: urban multi-locality and kin mobilization." Children's Geographies 13, no. 4 (2014): 482–95. http://dx.doi.org/10.1080/14733285.2014.890390.

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24

Shi, Guangyao, Hong Huang, Zhengying Li, and Yule Duan. "Multi-manifold locality graph preserving analysis for hyperspectral image classification." Neurocomputing 388 (May 2020): 45–59. http://dx.doi.org/10.1016/j.neucom.2019.12.112.

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25

Garde, Lisa, and Lena Greinke. "Using Participatory Visual Methods to Explore Multi-Local Living Arrangements - The Example of Work-Life Biographies and Reflexive Photography." International Journal of Qualitative Methods 21 (January 2022): 160940692211212. http://dx.doi.org/10.1177/16094069221121242.

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This paper explores work-related multi-locality – living in several places for work-related reasons – using qualitative interviews including participatory visual methods. To give space to multi-locals as people with complex living and working arrangements and to support their narratives, the visual methods work-life biographies and reflexive photography were used during interviews. After introducing the state of research on work-related multi-local living and visual methods, two German case studies, the one in the city of Stuttgart in Baden-Wuerttemberg, the other in the rural district of Diep

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26

Wang, Bin, Yu Liu, Wei Wang, Wei Xu, and Maojun Zhang. "Multi-Scale Locality-Constrained Spatiotemporal Coding for Local Feature Based Human Action Recognition." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/405645.

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We propose a Multiscale Locality-Constrained Spatiotemporal Coding (MLSC) method to improve the traditional bag of features (BoF) algorithm which ignores the spatiotemporal relationship of local features for human action recognition in video. To model this spatiotemporal relationship, MLSC involves the spatiotemporal position of local feature into feature coding processing. It projects local features into a sub space-time-volume (sub-STV) and encodes them with a locality-constrained linear coding. A group of sub-STV features obtained from one video with MLSC and max-pooling are used to classif

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27

Huang, Lei, Haoqiang Jin, Liqi Yi, and Barbara Chapman. "Enabling Locality-Aware Computations in OpenMP." Scientific Programming 18, no. 3-4 (2010): 169–81. http://dx.doi.org/10.1155/2010/185421.

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Locality of computation is key to obtaining high performance on a broad variety of parallel architectures and applications. It is moreover an essential component of strategies for energy-efficient computing. OpenMP is a widely available industry standard for shared memory programming. With the pervasive deployment of multi-core computers and the steady growth in core count, a productive programming model such as OpenMP is increasingly expected to play an important role in adapting applications to this new hardware. However, OpenMP does not provide the programmer with explicit means to program

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28

Ip, Manying, and Liangni Liu. "Gendered Factors of Chinese Multi-locality Migration: The New Zealand Case." Sites: a journal of social anthropology and cultural studies 5, no. 2 (2008): 31–56. http://dx.doi.org/10.11157/sites-vol5iss2id101.

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29

Song, Jianqiang, Xuemei Xie, Guangming Shi, and Weisheng Dong. "Multi-layer discriminative dictionary learning with locality constraint for image classification." Pattern Recognition 91 (July 2019): 135–46. http://dx.doi.org/10.1016/j.patcog.2019.02.018.

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30

Aljarah, Ibrahim, Maria Habib, Hossam Faris, et al. "A dynamic locality multi-objective salp swarm algorithm for feature selection." Computers & Industrial Engineering 147 (September 2020): 106628. http://dx.doi.org/10.1016/j.cie.2020.106628.

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31

Wang, Zhifang, Jiaqi Zhen, Yanchao Li, Guoqiang Li, and Qi Han. "Multi-feature Multimodal Biometric Recognition Based on Quaternion Locality Preserving Projection." Chinese Journal of Electronics 28, no. 4 (2019): 789–96. http://dx.doi.org/10.1049/cje.2019.05.006.

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32

Yu, Zhiqiang, Tingwei Wang, Xi Zhang, Jie Zhang, and Peng Ren. "Locality preserving fusion of multi-source images for sea-ice classification." Acta Oceanologica Sinica 38, no. 7 (2019): 129–36. http://dx.doi.org/10.1007/s13131-019-1464-2.

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33

Milliken, Pamela. "Multi-disciplinary team working in locality-based teams through COVID-19." International Journal of Integrated Care 22, S3 (2022): 357. http://dx.doi.org/10.5334/ijic.icic22361.

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34

Wang, Yingxu, Long Chen, Jin Zhou, Tianjun Li, and Yufeng Yu. "Low-rank kernel regression with preserved locality for multi-class analysis." Pattern Recognition 141 (September 2023): 109601. http://dx.doi.org/10.1016/j.patcog.2023.109601.

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35

Heinson, Graham, Ben Kay, Lachlan Loader, Kate Robertson, and Stephan Thiel. "A global magnetotelluric graphite type locality: Multi-decade, multi-scale studies of the Eyre Peninsula anomaly." Gondwana Research 105 (May 2022): 388–98. http://dx.doi.org/10.1016/j.gr.2021.09.017.

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36

Chen, Xiaoyun, Yeyuan Kang, and Zhiping Chen. "Multi-nonlinear multi-view locality-preserving projection with similarity learning for random cross-view gait recognition." Multimedia Systems 26, no. 6 (2020): 727–44. http://dx.doi.org/10.1007/s00530-020-00685-2.

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37

Vathi, Zana. "Local identities, identification and incorporation of Albanian immigrants in Florence." Modern Italy 17, no. 1 (2012): 51–67. http://dx.doi.org/10.1080/13532944.2010.524638.

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In this paper, the concepts of city as context, locality and incorporation are applied to the case of Albanian immigrants in Florence. To date, most research on the incorporation of immigrants and its relationship to space is based on the city or the inter-relations between the nation-state and city as contexts where incorporation takes place. The findings of this study show that the relationship between locality and the incorporation of immigrants is multi-dimensional. First, although findings support the role of the urban ethos in immigrants' incorporation, the city is not a homogeneous enti

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38

Rudraiah, Bhavya, and Geetha K S. "Multiple Detection and Tracking of Multi Class Vehicles using Locality Sensitive Histogram." International Journal of Recent Technology and Engineering 9, no. 5 (2021): 259–62. http://dx.doi.org/10.35940/ijrte.e5295.019521.

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Multiple object detection and tracking in a cluttered background is most important in vision-based applications. In this paper, the goal is to develop a classifier that detects and tracks multiple objects thereby ensuring robustness and accuracy. Locality Sensitive Histogram feature extraction is used, which adds contributions from all the pixels in an image. These features extracted are trained using decision tree classifier which performs with an accuracy of 97%. Experimental results demonstrate the objects tracked and detected under different scale and pose variations. Evaluation and compar

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39

FUJIKI, Takashi, Nobuhito MORI, and Koji KAWAGUCHI. "SEASONALITY AND LOCALITY OF THE MULTI-MODALITY ON THE DIRECTIONAL WAVE SPECTRUM." Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering) 75, no. 2 (2019): I_91—I_96. http://dx.doi.org/10.2208/kaigan.75.i_91.

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40

Wang, Yan, Luping Zhou, Biting Yu, et al. "3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis." IEEE Transactions on Medical Imaging 38, no. 6 (2019): 1328–39. http://dx.doi.org/10.1109/tmi.2018.2884053.

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41

Muthu Selvam, M., K. Mariappan, G. V. Sriramakrishnan, and G. Suseendran. "DeyPoS-homomorphism authenticated tree (HAT) for multi-user locality in cloud storage." International Journal of Engineering & Technology 7, no. 2.31 (2018): 141. http://dx.doi.org/10.14419/ijet.v7i2.31.13427.

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The technology PoS (Dynamic Proof of storage) is a cryptographic primordial allows a abuser to test the reliability of subcontracted documents and effectively replace documents in the cloud storage system. Despite the fact that investigators have projected several dynamic proofs of storage designs in distinct client settings, hassle in the multi-client settings have now not been examined adequately. In sensible multi-client cloud server storage space wishes a cozy client part cross client system of deduplication, it permits client toward bypass importing manner as well as gain instantly the ri

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42

Franco, Patrick, Rémy Mullot, and Valentin Owczarek. "Pareto multi-objective optimization for high locality-preserving space-filling curve identification." Swarm and Evolutionary Computation 92 (February 2025): 101797. https://doi.org/10.1016/j.swevo.2024.101797.

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43

Zhang, Xiaofei, Yongxin Tong, Lei Chen, Min Wang, and Shicong Feng. "Locality-aware allocation of multi-dimensional correlated files on the cloud platform." Distributed and Parallel Databases 33, no. 3 (2014): 353–80. http://dx.doi.org/10.1007/s10619-014-7153-y.

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44

Chen, Lanxiang, Nan Zhang, Kuan-Ching Li, Shuibing He, and Linbing Qiu. "Improving file locality in multi-keyword top-k search based on clustering." Soft Computing 22, no. 9 (2018): 3111–21. http://dx.doi.org/10.1007/s00500-018-3145-6.

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45

Ling, Ping, Xiangsheng Rong, Yongquan Dong, and Guosheng Hao. "Double-phase locality-sensitive hashing of neighborhood development for multi-relational data." Soft Computing 19, no. 6 (2014): 1553–65. http://dx.doi.org/10.1007/s00500-014-1343-4.

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46

Bhavya, Rudraiah, and Geetha K. S. Dr. "Multiple Detection and Tracking of Multi Class Vehicles using Locality Sensitive Histogram." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 5 (2021): 259–62. https://doi.org/10.35940/ijrte.E5295.019521.

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<strong>Abstract:</strong> Multiple object detection and tracking in a cluttered background is most important in vision-based applications. In this paper, the goal is to develop a classifier that detects and tracks multiple objects thereby ensuring robustness and accuracy. Locality Sensitive Histogram feature extraction is used, which adds contributions from all the pixels in an image. These features extracted are trained using decision tree classifier which performs with an accuracy of 97%. Experimental results demonstrate the objects tracked and detected under different scale and pose variat

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47

Tu, Youzhi, Man-Wai Mak, Kong-Aik Lee, and Weiwei Lin. "ConFusionformer: Locality-enhanced Conformer through multi-resolution attention fusion for speaker verification." Neurocomputing 644 (September 2025): 130429. https://doi.org/10.1016/j.neucom.2025.130429.

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48

YU, YI, KAZUKI JOE, VINCENT ORIA, FABIAN MOERCHEN, J. STEPHEN DOWNIE, and LEI CHEN. "MULTI-VERSION MUSIC SEARCH USING ACOUSTIC FEATURE UNION AND EXACT SOFT MAPPING." International Journal of Semantic Computing 03, no. 02 (2009): 209–34. http://dx.doi.org/10.1142/s1793351x09000732.

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Research on audio-based music retrieval has primarily concentrated on refining audio features to improve search quality. However, much less work has been done on improving the time efficiency of music audio searches. Representing music audio documents in an indexable format provides a mechanism for achieving efficiency. To address this issue, in this work Exact Locality Sensitive Mapping (ELSM) is suggested to join the concatenated feature sets and soft hash values. On this basis we propose audio-based music indexing techniques, ELSM and Soft Locality Sensitive Hash (SoftLSH) using an optimize

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49

Ling, Ping, Qiang Yang, and Xiang Yang You. "Formulate Neighborhood for Multi-Relational Data by Cell Accumulating." Key Engineering Materials 460-461 (January 2011): 165–71. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.165.

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This paper presents an algorithm to develop neighborhood, and the first time applies it into multi-relational (MR) data. The proposed algorithm is inspired by the idea of Locality Sensitiveness Hashing, whose idea is cell accumulating. The heuristics of parameterization are given, which are customized to MR data. Experiments demonstrate the proposed method behaves better than its peers on both MR data and common data.

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50

Sumet Mehta. "Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction." Lahore Garrison University Research Journal of Computer Science and Information Technology 4, no. 4 (2020): 55–72. http://dx.doi.org/10.54692/lgurjcsit.2020.0404109.

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In this paper, we propose a new dimension reduction (DR) algorithm called ensemble graph-based locality preserving projections (EGLPP); to overcome the neighborhood size k sensitivity in locally preserving projections (LPP). EGLPP constructs a homogeneous ensemble of adjacency graphs by varying neighborhood size k and finally uses the integrated embedded graph to optimize the low-dimensional projections. Furthermore, to appropriately handle the intrinsic geometrical structure of the multi-view data and overcome the dimensionality curse, we propose a generalized multi-manifold graph ensemble em

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