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Exploring Neighborhoods in the Metagenome Universe

Kathrin Aßhauer, Heiner Klingenberg, Thomas Lingner, Peter Meinicke
2014 International Journal of Molecular Sciences  
To make profile-based k-nearest-neighbor search and the 2D-visualization of the metagenome universe available to Int. J. Mol.  ...  The visualization method shows a similarly high accuracy in the reduced space as compared with the high-dimensional profile space.  ...  Except for the GO profile space, the City block metric generally showed a high accuracy and allows a fast calculation of distances as well as an intuitive interpretation.  ... 
doi:10.3390/ijms150712364 pmid:25026170 pmcid:PMC4139848 fatcat:stwxzbdz6vgrpbrcxec7bjyhji

Approximate k-nearest neighbors graph for single-cell Hi-C dimensional reduction with MinHash [article]

Joachim Wolff, Rolf Backofen, Bjoern Gruening
2020 bioRxiv   pre-print
However, the exact euclidean distance computation is in O(n^2) and therefore we present an implementation of an approximate nearest neighbors method based on local sensitive hashing running in O(n).  ...  A common approach to reduce the number of features is to compute a nearest neighbors graph.  ...  The exact mode of MinHash computes the euclidean distances on a pre-selection of neighbors and is therefore an approximate algorithm for an euclidean k-nearest neighbors graph.  ... 
doi:10.1101/2020.03.05.978569 fatcat:duun2ajf3nd3hgajcsed7hdzia

Confirmation Sampling for Exact Nearest Neighbor Search [article]

Tobias Christiani, Rasmus Pagh, Mikkel Thorup
2018 arXiv   pre-print
Locality-sensitive hashing (LSH), introduced by Indyk and Motwani in STOC '98, has been an extremely influential framework for nearest neighbor search in high-dimensional data sets.  ...  While theoretical work has focused on the approximate nearest neighbor problems, in practice LSH data structures with suitably chosen parameters are used to solve the exact nearest neighbor problem (with  ...  [14] propose to select parameters based on the "distance profile" of a data set, but needs a bound on the distance to the nearest neighbor to function.  ... 
arXiv:1812.02603v1 fatcat:akod4v7g6zgkrhu2h4wxqvyliy

Tree approximation of the long wave radiation parameterization in the NCAR CAM global climate model

Alexei Belochitski, Peter Binev, Ronald DeVore, Michael Fox-Rabinovitz, Vladimir Krasnopolsky, Philipp Lamby
2011 Journal of Computational and Applied Mathematics  
nearest neighbors.  ...  Unfortunately in high dimensions there are no fast algorithms which could answer the question "what are the nearest neighbors to a given query point x?".  ...  Sparse occupancy trees Sparse occupancy trees have been developed as an alternative for approximate nearest neighbor methods (see [6] ).  ... 
doi:10.1016/ fatcat:5dvqnovbinc7zbwihpct27hbsq

Joint Geodesic Upsampling of Depth Images

Ming-Yu Liu, Oncel Tuzel, Yuichi Taguchi
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
We propose an algorithm utilizing geodesic distances to upsample a low resolution depth image using a registered high resolution color image.  ...  Though this is closely related to the all-pairshortest-path problem which has O(n 2 log n) complexity, we develop a novel approximation algorithm whose complexity grows linearly with the image size and  ...  We analyzed the approximation quality by comparing it with the the exact algorithm, which find the K nearest neighbors with O(n 2 log n) complexity.  ... 
doi:10.1109/cvpr.2013.29 dblp:conf/cvpr/0001TT13 fatcat:k2jnsbmoxffwjaa4dly4wruuzq

Evaluating the Eccentricities of Poplar Stem Profiles with Terrestrial Laser Scanning

Nicola Puletti, Mirko Grotti, Roberto Scotti
2019 Forests  
The estimated profiles display high variability with an average of 1.6 cm of lateral compression.  ...  These functions are developed based on measures of stem diameters taken at different distances from the base.  ...  In this plantation, distances to neighboring trees differ depending on direction. Lateral compression is then interpreted and assumed as an effect of this anisotropic competition.  ... 
doi:10.3390/f10030239 fatcat:t5j2h4loljdh7e7d6yso6xcw5i

Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs

Joachim Wolff, Rolf Backofen, Björn Grüning
2021 Bioinformatics  
We present a single-cell Hi-C clustering approach using an approximate nearest neighbors method based on locality-sensitive hashing to reduce the dimensions and the computational resources.  ...  The approximate nearest neighbors implementation is available via and as a conda package via the bioconda channel.  ...  We thank Milad Miladi and Fabrizio Costa for the supervision of a first implementation of the sparse-neighbors-search library during the master thesis of J.W. in the winter semester 2015/2016.  ... 
doi:10.1093/bioinformatics/btab394 pmid:34021764 fatcat:cp62sdetwnhhtglgadtzp6nb6u

A fast algorithm for complex discord searches in time series: HOT SAX Time [article]

Paolo Avogadro, Matteo Alessandro Dominoni
2021 arXiv   pre-print
In recent years, fast approximate algorithms for discord search have been proposed in order to compensate for the increasing size of the time series.  ...  Keogh for providing the dataset used for Sec. 4.6 and for the useful information regarding the Matrix Profile.  ...  The nearest neighbor distance for the 10th discord from the sample is used as the r parameter for DADD. One does not expect to obtain the exact nnd of the k-th discord but just an approximation.  ... 
arXiv:2101.10698v1 fatcat:cpqxplamsjafpo6daghgcnp5jy

Survey on Clustering High-Dimensional data using Hubness

Miss. Archana Chaudahri, Mr. Nilesh Vani
2020 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
One such phenomenon, related to nearest-neighbor learning methods, is known as hubness and refers to the emergence of very influential nodes (hubs) in k-nearest neighbor graphs.  ...  A crisp weighted voting scheme for the k-nearest neighbor classifier has recently been proposed which exploits this notion.  ...  Hub-Based Clustering Hubness, which is the tendency of some data points in high-dimensional data sets to occur much more frequently in k-nearest neighbor lists of other points than the rest of the points  ... 
doi:10.32628/cseit195671 fatcat:ajsps7bihvhmvavebabm3qeyim

Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets

Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, Eamonn Keogh
2016 2016 IEEE 16th International Conference on Data Mining (ICDM)  
For exceptionally large datasets, the algorithm can be trivially cast as an anytime algorithm and produce highquality approximate solutions in reasonable time.  ...  Because each subsequence's distance profile is bounded below by the exact matrix profile, updating an approximate matrix profile with a distance profile with pairwise minimum operation either drives the  ...  Profile-Based Discord Discovery A time series discord is the subsequence that has the maximum distance to its nearest neighbor.  ... 
doi:10.1109/icdm.2016.0179 dblp:conf/icdm/YehZUBDDSMK16 fatcat:klz65rqgfncepbw75zqh5wcgz4

Distance Transforms on Anisotropic Surfaces for Surface Roughness Measurement [chapter]

Leena Ikonen, Toni Kuparinen, Eduardo Villanueva, Pekka Toivanen
2006 Lecture Notes in Computer Science  
The distance transform combined with a nearest neighbor transform produces a roughness map showing the average roughness of image regions in addition to one roughness value for the whole surface.  ...  In this article, the DTOCS is generalized for surfaces represented as real altitude data in an anisotropic grid.  ...  Distance and Nearest Neighbor Transformation An efficient priority pixel queue transformation algorithm for calculating the DTOCS is presented in [14] .  ... 
doi:10.1007/11907350_55 fatcat:oy4grfsvzjhthczw7cecqb5oum

Content-based crowd retrieval on the real-time web

Krishna Y. Kamath, James Caverlee
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
crowds as compared to an offline approach.  ...  Through extensive experimental study, we find significantly more efficient crowd discovery as compared to both a k-means clustering-based approach and a MapReduce-based implementation, while maintaining high-quality  ...  Nearest-neighbor and approximate nearest-neighbor search in a high-dimensional vector space is a difficult problem that Indyk and Motwani [15, 13] approach through the use of a family of randomized hash  ... 
doi:10.1145/2396761.2396789 dblp:conf/cikm/KamathC12 fatcat:tgeiyomoabhdvc5o4pf76r7aam

Optimization of Breast Tomosynthesis Visualization through 3D Volume Rendering

Ana M. Mota, Matthew J. Clarkson, Pedro Almeida, Nuno Matela
2020 Journal of Imaging  
Hamming interpolation function presented the best compromise in image quality. The sampling distance values that showed a better balance between time and image quality were 0.025 mm and 0.05 mm.  ...  Rendering parameters directly influence the quality of rendered images.  ...  Study of Image Interpolators Linear, Cubic, and Nearest-Neighbor Interpolation The 5.0 mm disk's profiles obtained after rescaling with the linear, cubic, and nearest-neighbor interpolators are presented  ... 
doi:10.3390/jimaging6070064 pmid:34460657 fatcat:6mic64a775fh3d3w4vu255gsl4

Context-Aware Nearest Neighbor Query on Social Networks [chapter]

Yazhe Wang, Baihua Zheng
2011 Lecture Notes in Computer Science  
In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to retrieve the nearest node to the query node that matches the context specified  ...  CANN considers both the structure of the social network, and the profile information of the nodes. We design a hyper-graph based index structure to support approximated CANN search efficiently.  ...  A context-aware nearest neighbor (CANN) query is defined to search over social network based on both network structure and the profile information.  ... 
doi:10.1007/978-3-642-24704-0_15 fatcat:ehj2ockgfndpdg4bk6qhug44ma

Natural Variability [chapter]

2017 Encyclopedia of GIS  
Approximate nearest neighbors methods for learning and vision, 13 Dec 2003 Berchtold S, Keim DA, Kriegel H-P (1996) The X-tree: an index structure for high-dimensional data.  ...  Current research in nearest-neighbor algorithms is concerned with determining ways to approximate the solution and using nearest-neighbors as an approximation for other problems.  ...  High performance computing provides the necessary computing power to support a timeconsuming network GIS.  ... 
doi:10.1007/978-3-319-17885-1_101534 fatcat:aj3jjedtq5ezbpoakmsuxw7pba
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