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Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

Hadi Santoso, Agus Harjoko, Agfianto Eko
2017 International Journal of Advanced Computer Science and Applications  
(PCA) to extract facial features followed by applying the priority of k-d tree search to speed up the process of facial classification using k-Nearest Neighbor.  ...  This research looks for the best value of k to get the right facial recognition using k-fold cross-validation. 10-fold cross-validation at level 3 DWT-PCA shows that face recognition using k-Nearest Neighbor  ...  It can be used with a k-nearest neighbor (k-NN) approach to match facial features efficiently and search for the location of the nearest neighbors.  ... 
doi:10.14569/ijacsa.2017.081115 fatcat:sz4wf4mbbrdajejzx5sngzopz4

Computationally Efficient Learning of Statistical Manifolds [article]

Fan Cheng, Anastasios Panagiotelis, Rob J Hyndman
2022 arXiv   pre-print
neighbor searching, could largely improve the computational efficiency with little to no loss in the accuracy of manifold embedding.  ...  Analyzing high-dimensional data with manifold learning algorithms often requires searching for the nearest neighbors of all observations.  ...  Acknowledgment This research was supported in part by the Monash eResearch Centre and eSolutions-Research Support Services through the use of the MonARCH HPC Cluster.  ... 
arXiv:2103.11773v2 fatcat:xcmem2vwcrgongpke4gd4gsdfa

On k-Nearest Neighbor Searching in Non-Ordered Discrete Data Spaces

Dashiell Kolbe, Qiang Zhu, Sakti Pramanik
2007 2007 IEEE 23rd International Conference on Data Engineering  
Numerous techniques have been proposed in the past for supporting efficient k-NN searches in continuous data spaces.  ...  No such work has been reported in the literature for k-NN searches in a non-ordered discrete data space (NDDS). Performing k-NN searches in an NDDS raises new challenges.  ...  Acknowledgments The authors of this paper wish to extend their gratitude towards Gang Qian, Changqing Chen and Chad Klochko for their help in developing experimental programs and conducting some experiments  ... 
doi:10.1109/icde.2007.367888 dblp:conf/icde/KolbeZP07 fatcat:kep5xwp3ujh5tc4nrq626bpcwq

Efficient Spatial Nearest Neighbor Queries Based on Multi-layer Voronoi Diagrams [article]

Yang Li, Gang Liu, Junbin Gao, Zhenwen He, Mingyuan Bai, Chengjun Li
2019 arXiv   pre-print
A direct generalization of the NN query is the k nearest neighbors (kNN) query, where the k closest point are required to be found.  ...  In the experiments, we evaluate the efficiency of this indexing for both NN search and kNN search by comparing with VoR-tree, R-tree and kd-tree.  ...  We use the experiment of 10,000 discrete points to list the average number of Voronoi neighbors for every point for the dimensions between 2 to 6 as in Table IV .  ... 
arXiv:1911.02788v1 fatcat:pxv6wnj7gzgd3cgrddv4w5b3ei

Discrete Point Cloud Filtering And Searching Based On VGSO Algorithm

Fengjun Hu, Yanwei Zhao, Wanliang Wang, Xianping Huang
2013 ECMS 2013 Proceedings edited by: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang  
the voxel with the centroid of all points; then, the neighborhood of discrete points is analyzed statistically, calculating average distance of every point to its neighboring points and filtering the  ...  The massive point cloud data obtained through the computer vision is uneven in density together with a lot of noise and outliers, which will greatly reduce the point cloud search efficiency and affect  ...  The nearest neighbor search (NNS), also known as "the closest point search ", is an optimization problem to find the nearest points in scale space.  ... 
doi:10.7148/2013-0850 dblp:conf/ecms/HuZWH13 fatcat:hq4hvgjyxvdu3dylchjawcsz4q

Efficient Data Structures for Model-free Data-Driven Computational Mechanics [article]

Robert Eggersmann, Laurent Stainier, Michael Ortiz, Stefanie Reese
2020 arXiv   pre-print
In this study, we compare and develop several possible data structures for solving the nearest-neighbor problem.  ...  We show that computations on a single processor with up to one billion material data points are feasible within a few seconds execution time with a speedup of more than 106 with respect to exact k-d trees  ...  modeling of polymorphic uncertainty in the context of robustness and reliability" within the priority program SPP 1886 "Polymorphic uncertainty modelling for the numerical design of structures".  ... 
arXiv:2012.00357v1 fatcat:q4wccv23gfcepfn3memq6ziucm

Efficient k-nearest neighbor searching in nonordered discrete data spaces

Dashiell Kolbe, Qiang Zhu, Sakti Pramanik
2010 ACM Transactions on Information Systems  
Numerous techniques have been proposed in the past for supporting efficient k-nearest neighbor (k-NN) queries in continuous data spaces.  ...  Limited work has been reported in the literature for k-NN queries in a non-ordered discrete data space (NDDS). Performing k-NN queries in an NDDS raises new challenges.  ...  ACKNOWLEDGMENTS The authors of this paper wish to extend their gratitude towards Gang Qian for his help in developing experimental programs.  ... 
doi:10.1145/1740592.1740595 fatcat:vouylrmln5aqbeci2l5es37ap4

CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree

2019 International journal of recent technology and engineering  
The co-located instances are mapped onto a K-dimensional tree (K-d tree) inorder to make the querying process efficient. The algorithm is analyzed using a hypothetical data set generated through QGIS  ...  Hence the main aim of this work is to extend the K-Nearest Neighbor (KNN) querying to co-located instances for context aware based querying or location-based services (LBS).  ...  The purpose of this work is to use the mined co-located patterns for a KNN [15] querying. The K nearest neighbor (KNN) is used to find the nearest K neighbors for a query from a given dataset.  ... 
doi:10.35940/ijrte.b1741.078219 fatcat:ebqamcboxvdijlja5t5lutz5xi

RRTPI: Policy iteration on continuous domains using rapidly-exploring random trees

Manimaran Sivasamy Sivamurugan, Balaraman Ravindran
2014 2014 IEEE International Conference on Robotics and Automation (ICRA)  
Path planning in continuous spaces has been a central problem in robotics.  ...  In the case of systems with complex dynamics, the performance of sampling based techniques relies on identifying a good approximation to the cost-to-go distance metric.  ...  The distance metric used to evaluate the nearest neighbors is Euclidean. J(x) = ∑ s i ∈Nbr(x) 1 k J(s i ) For k = 1, the value of a state is generalized to its Voronoi region.  ... 
doi:10.1109/icra.2014.6907494 dblp:conf/icra/SivamuruganR14 fatcat:5cbwex3ssnbr3c42f4zvzouxlq

Nearest Neighbor Retrieval Using Distance-Based Hashing

Vassilis Athitsos, Michalis Potamias, Panagiotis Papapetrou, George Kollios
2008 2008 IEEE 24th International Conference on Data Engineering  
A method is proposed for indexing spaces with arbitrary distance measures, so as to achieve efficient approximate nearest neighbor retrieval.  ...  Hashing methods, such as Locality Sensitive Hashing (LSH), have been successfully applied for similarity indexing in vector spaces and string spaces under the Hamming distance.  ...  Theoretically, for a database of n vectors of d dimensions, the time complexity of finding the nearest neighbor of an object using LSH is sublinear in n and only polynomial in d.  ... 
doi:10.1109/icde.2008.4497441 dblp:conf/icde/AthitsosPPK08 fatcat:avkrunihj5ep5pqmivm6vttjoq

Exact and/or Fast Nearest Neighbors [article]

Matthew Francis-Landau, Benjamin Van Durme
2019 arXiv   pre-print
Prior methods for retrieval of nearest neighbors in high dimensions are fast and approximate--providing probabilistic guarantees of returning the correct answer--or slow and exact performing an exhaustive  ...  We present Certified Cosine, a novel approach to nearest-neighbors which takes advantage of structure present in the cosine similarity distance metric to offer certificates.  ...  Introduction Abstractly, the nearest neighbor problem is defined as given a query q ∈ R n , find the nearest vector v i ∈ V from a discrete set of points according to a distance function d(x, y) (argmin  ... 
arXiv:1910.02478v2 fatcat:nczh3emugnhajaxjouulhqumqq

All Near Neighbor GraphWithout Searching

Edgar Chávez, Verónica Ludueña, Nora Reyes, Fernando Kasián
2018 Journal of Computer Science and Technology  
Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection.  ...  In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects.  ...  A collection of sites or objects is given, and the objective is to find, for each object the nearest neighbor in the collection.  ... 
doi:10.24215/16666038.18.e07 fatcat:4rhpo2id7bgjxavab2xtijwrsq

k-Nearest neighbor searching in hybrid spaces

Dashiell Kolbe, Qiang Zhu, Sakti Pramanik
2014 Information Systems  
Little work has been reported in the literature to support k-nearest neighbor (k-NN) searches/ queries in hybrid data spaces (HDS).  ...  In this paper, we present an algorithm for k-NN searches using a multidimensional index structure in hybrid data spaces.  ...  of Michigan.  ... 
doi:10.1016/j.is.2014.02.004 fatcat:dwh4h3oa7vdcfen5spg3aat6o4

Research on Time Series Query Method Based on Linear Hash Index

XI LU, XIN-AI XU
2017 DEStech Transactions on Engineering and Technology Research  
In the query phase, the method of combining the K nearest neighbor and the lower bound distance is used to filter out the redundant results.  ...  In this paper, we propose a new query processing method for time series, in order to reduce the index creation time and improve query efficiency.  ...  K nearest neighbor query will approximate query results as K nearest neighbors in the data set, when the input of new time series, in the time series data set and find the target time sequence nearest  ... 
doi:10.12783/dtetr/mcee2017/15798 fatcat:ajfb25bfpbdqlmdw3re7wtnctu

Thick boundaries in binary space and their influence on nearest-neighbor search

Tomasz Trzcinski, Vincent Lepetit, Pascal Fua
2012 Pattern Recognition Letters  
In favorable cases, the binary vectors can be used as indices to directly access their nearest neighbors [17] which provides sub-linear complexity of the search.  ...  In the case of binary spaces, the thick boundaries of the Voronoi diagram influence the search regardless of the data dimensionality, as we explain in details in Section 3.  ...  Approximate Nearest Neighbor Search Even though Nearest Neighbor search has been widely discussed in the literature, no known generic algorithm is both exact and more efficient than brute force search.  ... 
doi:10.1016/j.patrec.2012.08.006 fatcat:3znq765zt5gl7omss2khpy4zbm
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