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Subspace similarity search using the ideas of ranking and top-k retrieval

Thomas Bernecker, Tobias Emrich, Franz Graf, Hans-Peter Kriegel, Peer Kroger, Matthias Renz, Erich Schubert, Arthur Zimek
2010 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)  
Here, we propose the first index-based solution to subspace similarity search in arbitrary subspaces which is based on the concepts of nearest neighbor ranking and top-k retrieval.  ...  There are abundant scenarios for applications of similarity search in databases where the similarity of objects is defined for a subset of attributes, i.e., in a subspace, only.  ...  They are funded by the German Federal Ministry of Economics and Technology under the grant number 01MQ07020. The responsibility for this publication lies with the authors.  ... 
doi:10.1109/icdew.2010.5452771 dblp:conf/icde/BerneckerEGKKRSZ10 fatcat:6th4regexrgctc23gaa5gzwobe

Subspace Similarity Search: Efficient k-NN Queries in Arbitrary Subspaces [chapter]

Thomas Bernecker, Tobias Emrich, Franz Graf, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Erich Schubert, Arthur Zimek
2010 Lecture Notes in Computer Science  
The three existing approaches are variations of the sequential scan. Here, we propose the first index-based solution to subspace similarity search in arbitrary subspaces.  ...  There are abundant scenarios for applications of similarity search in databases where the similarity of objects is defined for a subset of attributes, i.e., in a subspace, only.  ...  Acknowledgements This research has been supported in part by the THESEUS program. They are funded by the German Federal Ministry of Economics and Technology under the grant number 01MQ07020.  ... 
doi:10.1007/978-3-642-13818-8_38 fatcat:cg4mrpld5jgohil43xmokufuga

Personalizing Web Search Results Based on Subspace Projection [chapter]

Jingfei Li, Dawei Song, Peng Zhang, Ji-Rong Wen, Zhicheng Dou
2014 Lecture Notes in Computer Science  
The personalized scores are then used to re-rank the documents through the Borda' ranking fusion method.  ...  the user profile subspace.  ...  The top K eigenvectors constitute a basis of user profile subspace corresponding to the main aspects of user's search history.  ... 
doi:10.1007/978-3-319-12844-3_14 fatcat:nq5bpmzxorefvamovstihd733u

Query reranking as a service

Abolfazl Asudeh, Nan Zhang, Gautam Das
2016 Proceedings of the VLDB Endowment  
Many web databases are "hidden" behind proprietary search interfaces that enforce the top-k output constraint, i.e., each query returns at most k of all matching tuples, preferentially selected and returned  ...  Since skyline tuples provide critical insights into the database and include the top-ranked tuple for every possible ranking function following the monotonic order of attribute values, skyline discovery  ...  ∀n > 1, there exists a database of n tuples such that finding the top-ranked tuple on an attribute through a top-k search interface requires at least n/k queries that retrieve all the n tuples.  ... 
doi:10.14778/2983200.2983205 fatcat:syqixohsajhazbddxdi5wkdoim

Efficient Retrieval of Matrix Factorization-Based Top-k Recommendations: A Survey of Recent Approaches

Dung D. Le, Hady Lauw
2021 The Journal of Artificial Intelligence Research  
and returns the top-k items from the ranked list.  ...  Top-k recommendation seeks to deliver a personalized list of k items to each individual user.  ...  Cai, 2016) are based on the k−NN graph, which is an approximation of the Delaunay Graph.  ... 
doi:10.1613/jair.1.12403 fatcat:fpum5xffmbhclme3hdmmbs34uy

Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion [chapter]

Michael Hund, Michael Behrisch, Ines Färber, Michael Sedlmair, Tobias Schreck, Thomas Seidl, Daniel Keim
2015 Lecture Notes in Computer Science  
Computing the similarity between objects is a central task for many applications in the field of information retrieval and data mining.  ...  For finding k-nearest neighbors, typically a ranking is computed based on a predetermined set of data dimensions and a distance function, constant over all possible queries.  ...  We would like to thank the German Research Foundation (DFG) for financial support within the projects A03 of SFB/Transregio 161 "Quantitative Methods for Visual Computing" and DFG-664/11 "SteerSCiVA: Steerable  ... 
doi:10.1007/978-3-319-25087-8_29 fatcat:tugiqueqxncrnmnatl6xbmyfiq

Random subspace for binary codes learning in large scale image retrieval

Cong Leng, Jian Cheng, Hanqing Lu
2014 Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14  
In these approaches, the information caught in different dimensions is unbalanced and generally most of the information is contained in the top eigenvectors.  ...  At first, a small fraction of the whole feature space is randomly sampled to train the hashing algorithms each time and only the top eigenvectors are kept to generate one piece of short code.  ...  Anchor Graph Hashing (AGH) [7] follows the same idea of SH but utilizes anchor graph to obtain tractable low-rank adjacency matrices.  ... 
doi:10.1145/2600428.2609502 dblp:conf/sigir/LengCL14 fatcat:2kkqcld2yzevlc67tthghcs55u

From keywords to keyqueries

Tim Gollub, Matthias Hagen, Maximilian Michel, Benno Stein
2013 Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '13  
Keyqueries are defined implicitly by the index and the retrieval model of a reference search engine: keyqueries for a document are the minimal queries that return the document in the top result ranks.  ...  Besides applications in the fields of information retrieval and data mining, keyqueries have the potential to form the basis of a dynamic classification system for future digital libraries-the modern version  ...  The subspace of too-general-queries that do not retrieve the desired document in the top-k results, the subspace Q * of keyqueries, and the subspace of too-specific-queries all of which are supersets of  ... 
doi:10.1145/2484028.2484181 dblp:conf/sigir/GollubHMS13 fatcat:4kefpe37brcwdnd7trktwjdg54

Locality condensation

Zi Huang, Heng Tao Shen, Jie Shao, Stefan Rüger, Xiaofang Zhou
2008 Proceeding of the 16th ACM international conference on Multimedia - MM '08  
Consequently, for similarity search in the subspace, the number of false hits (i.e., distant points that are falsely retrieved) will be reduced.  ...  Reducing the dimensionality is an important means to tackle the problem. In this paper, we study dimensionality reduction for top-k image retrieval.  ...  In particular, top-k retrieval (or k-nearest neighbor search) finds the k most similar objects with respect to a query [24, 15, 6, 21, 3] .  ... 
doi:10.1145/1459359.1459389 dblp:conf/mm/HuangSSRZ08 fatcat:yd5xhyh5q5bqjcbci2clvtegd4

Automatic Spatially-aware Fashion Concept Discovery [article]

Xintong Han, Zuxuan Wu, Phoenix X. Huang, Xiao Zhang, Menglong Zhu, Yuan Li, Yang Zhao, Larry S. Davis
2017 arXiv   pre-print
We conducted extensive experiments on our newly collected Fashion200K dataset, and results on clustering quality evaluation and attribute-feedback product retrieval task demonstrate the effectiveness of  ...  Finally, we decompose the visual-semantic embedding space into multiple concept-specific subspaces, which facilitates structured browsing and attribute-feedback product retrieval by exploiting multimodal  ...  Acknowledgement The authors acknowledge the Maryland Advanced Research Computing Center (MARCC) for providing computing resources.  ... 
arXiv:1708.01311v1 fatcat:exsjmxh7vzfidfea62igpunyfe

Learning Query and Image Similarities with Ranking Canonical Correlation Analysis

Ting Yao, Tao Mei, Chong-Wah Ngo
2015 2015 IEEE International Conference on Computer Vision (ICCV)  
One of the fundamental problems in image search is to learn the ranking functions, i.e., similarity between the query and image.  ...  We demonstrate in this paper that the above two limitations can be well mitigated by jointly exploring subspace learning and the use of click-through data.  ...  Acknowledgments The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 120213).  ... 
doi:10.1109/iccv.2015.12 dblp:conf/iccv/YaoMN15 fatcat:awfewjixsbbkbfaenxe7mqwugm

Term Ranking for Clustering Web Search Results

Fatih Gelgi, Hasan Davulcu, Srinivas Vadrevu
2007 International Workshop on the Web and Databases  
TermRank achieves desirable ranking of discriminative terms higher than the ambiguous terms, and ranking ambiguous terms higher than common terms.  ...  First, we show that one cannot readily import the frequency based feature ranking to cluster the web search results as in the text document clustering.  ...  The top-200 terms ranked by TF, TF/IDF and TermRank have been used as feature vectors in K-means and SCuBA to determine the effect of ranking methods and compare their qualities.  ... 
dblp:conf/webdb/GelgiDV07 fatcat:teurefutwjbcfnkxlqti5u4frq

cBiK: A Space-Efficient Data Structure for Spatial Keyword Queries

Carlos E. SanJuan-Contreras, Gilberto Gutierrez R., Miguel A. Martinez-Prieto, Diego Seco
2020 IEEE Access  
Due to the large volume of data, the use of indexes to speed up the queries that facilitate such analyses is imperative.  ...  Our experimental evaluation, shows that this approach needs half the space and is more than one order of magnitude faster than a disk resident state-of-the-art index.  ...  ACKNOWLEDGMENT The authors would like to thank José R. Paramá (University of A Coruña, Spain) for his helpful advice and the original ideas that motivated this article.  ... 
doi:10.1109/access.2020.2997258 fatcat:d4upocxmdvbdjhlaizuuyr7bmi

Top-k Similarity Search over Gaussian Distributions Based on KL-Divergence

Tingting Dong, Yoshiharu Ishikawa, Chuan Xiao
2016 Journal of Information Processing  
By employing Kullback-Leibler divergence (KL-divergence) to measure the dissimilarity between two Gaussian distributions, our goal is to search a database for the top-k Gaussian distributions similar to  ...  The problem of similarity search is a crucial task in many real-world applications such as multimedia databases, data mining, and bioinformatics.  ...  Based on the analysis, we proposed two types of approaches to efficiently and effectively process top-k similarity search over Gaussian distributions, which returns the k most similar ones to a given query  ... 
doi:10.2197/ipsjjip.24.152 fatcat:4b7w7yml5rcn3oxfefgxo5r4mm

Optimal Factor Analysis and Applications to Content-Based Image Retrieval [chapter]

Yuhua Zhu, Washington Mio, Xiuwen Liu
2008 Communications in Computer and Information Science  
The methods are applied to content-based image categorization and retrieval using a representation of images by histograms of their spectral components.  ...  Various experiments are carried out and the results are compared to those that have been previously reported for some other image retrieval systems.  ...  This work was supported in part by NSF grants CCF-0514743 and IIS-0307998.  ... 
doi:10.1007/978-3-540-89682-1_12 fatcat:6ntexp6u6jfqbcqepuvhnvmwzm
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