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When Is "Nearest Neighbor" Meaningful? [chapter]

Kevin Beyer, Jonathan Goldstein, Raghu Ramakrishnan, Uri Shaft
1999 Lecture Notes in Computer Science  
In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful.  ...  We explore the e ect of dimensionality on the \nearest neighbor" problem.  ...  Implicitly Low Dimensionality Another possible scenario where high dimensional nearest neighbor queries are meaningful occurs when the underlying dimensionality of the data is much lower than the actual  ... 
doi:10.1007/3-540-49257-7_15 fatcat:74jzytwcordifhsz4tqryauhcm

When Is Nearest Neighbors Indexable? [chapter]

Uri Shaft, Raghu Ramakrishnan
2004 Lecture Notes in Computer Science  
It is known that under broad conditions, nearest neighbors workloads become unstable-distances between data points become indistinguishable from each other.  ...  In this paper, we consider whether traditional index structures are effective in processing unstable nearest neighbors workloads.  ...  The Nearest Neighbors Indexing Theorem In this section we present the nearest neighbors indexing theorem.  ... 
doi:10.1007/978-3-540-30570-5_11 fatcat:55e7ofbn7nhhtfcf3dfu22hhha

Teaching Meaningful Explanations [article]

Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
2018 arXiv   pre-print
Results demonstrate that meaningful explanations can be reliably taught to machine learning algorithms, and in some cases, also improve modeling accuracy.  ...  A joint model is then learned to produce both labels and explanations from the input features.  ...  to the user the nearest neighbors and any relevant information regarding them.  ... 
arXiv:1805.11648v2 fatcat:gucmjbibo5dl3nrgzojxumscwy

High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors

Dusan Omercevic, Ondrej Drbohlav, Ales Leonardis
2007 2007 IEEE 11th International Conference on Computer Vision  
In our approach, we require every nearest neighbor to be meaningful, that is, sufficiently close to a query feature such that it is an outlier to a background feature distribution.  ...  Matching of high-dimensional features using nearest neighbors search is an important part of image matching methods which are based on local invariant features.  ...  The final weight c i is computed as c i = s i t i . (6) When applied to image matching, meaningful nearest neighbors are independently weighted for each query feature.  ... 
doi:10.1109/iccv.2007.4408880 dblp:conf/iccv/OmercevicDL07 fatcat:enut52peyvdkjgvtkinzties4q

Multiobjective Optimization for Meaningful Metrical Poetry

Fahrurrozi Rahman, Ruli Manurung
2011 International Conference on Computational Creativity  
Various results show that it consistently outperforms our previous system in its ability to generate a meaningful metrical text according to given semantic and metre specifications, and in some cases is  ...  However, it is still unable to reach the goal of generating an entire poem. We conclude with suggestions for further work to address this shortcoming.  ...  The density is obtained using an adaptation of the k-th nearest neighbor method, where the density estimation is the inverse of the distance to the k-th nearest neighbor : D(i) = 1 σ k i + 2 Here σ k i  ... 
dblp:conf/icccrea/RahmanM11 fatcat:hg7uhffjybglhmsntln4fdin3e

Word Embedding Calculus in Meaningful Ultradense Subspaces

Sascha Rothe, Hinrich Schütze
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
The first operation computes the antonym of word v: antonym(v) = nn(u v − 2u p v ) (4) where nn : R d → V returns the word whose embedding is the nearest neighbor to the input.  ...  To evaluate PSC-SET, we calculate the 10 nearest neighbors of the m words in the spectrum and rank the 10m neighbors by the distance to our spectrum, i.e., the cosine distance in the orthogonal complement  ... 
doi:10.18653/v1/p16-2083 dblp:conf/acl/RotheS16 fatcat:ewxoguoz3fcoxfqxjkjnm6hyyq

When is Early Classification of Time Series Meaningful? [article]

Renjie Wu, Audrey Der, Eamonn J. Keogh
2021 arXiv   pre-print
The problem is not with the algorithms per se but with the vague and underspecified problem description.  ...  The idea is that the earlier classification would allow us to take immediate action, in a domain in which some practical interventions are possible.  ...  Fig. 8 . 8 (left) A template for dustbathing and its 500 nearest neighbors. (center) A truncated version of the template and its 500 nearest neighbors.  ... 
arXiv:2102.11487v1 fatcat:75bjzltkkrcmxp4cqgenvnnqyi

Discovering personally meaningful places

Changqing Zhou, Dan Frankowski, Pamela Ludford, Shashi Shekhar, Loren Terveen
2007 ACM Transactions on Information Systems  
It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places.  ...  This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, "home", "work" or "Northwest  ...  if any neighbor belongs to the existing cluster.  ... 
doi:10.1145/1247715.1247718 fatcat:bt33nkxf7jg6ze32ddisthhw4y

Discovery of Meaningful Rules in Time Series

Mohammad Shokoohi-Yekta, Yanping Chen, Bilson Campana, Bing Hu, Jesin Zakaria, Eamonn Keogh
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
The ability to make predictions about future events is at the heart of much of science; so, it is not surprising that prediction has been a topic of great interest in the data mining community for the  ...  Beyond our novel definitions/representations, which allow for meaningful and extendable specifications of rules, we further show novel algorithms that allow us to quickly discover high quality rules in  ...  This finding bolsters our confidence that our scoring function is valid. Time Complexity If maxlag is set to zero, then the time complexity for our algorithm is O(nlogn).  ... 
doi:10.1145/2783258.2783306 dblp:conf/kdd/Shokoohi-Yekta015 fatcat:p26zqf5dvzabrdowvx6up2teeu

Creating small but meaningful representations of digital pathology images

Corentin Gueréndel, Phil Arnold, Ben Torben-Nielsen
2021 Workshop on Computational Pathology  
Representation learning is a popular application of deep learning where an object (e.g., an image) is converted into a lower-dimensional representation that still encodes relevant features of the original  ...  Then, the final slide-level representation is a function of the proportional cluster membership of all tiles from one WSI.  ...  Left: the probability of retrieving N nearest neighbors and having at least one of the same tissue type (the "hit rate").  ... 
dblp:conf/compay/GuerendelAT21 fatcat:rebqm3b2zfau5k6s3gm7xkehwe

From frequent itemsets to semantically meaningful visual patterns

Junsong Yuan, Ying Wu, Ming Yang
2007 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07  
It is not a trivial task to discover meaningful visual patterns in image databases, because the content variations and spatial dependency in the visual data greatly challenge most existing methods.  ...  This paper presents a novel approach to coping with these difficulties for mining meaningful visual patterns.  ...  For example, Gi can be the spatial K-nearest neighbors (K-NN) or -nearest neighbors of vi ( -NN) under Euclidean distance.  ... 
doi:10.1145/1281192.1281284 dblp:conf/kdd/YuanWY07 fatcat:6ipqw3amcngivmepdjjkx2i3gq

Early Classification of Time Series is Meaningful [article]

Youssef Achenchabe, Alexis Bondu, Antoine Cornuéjols, Vincent Lemaire
2021 arXiv   pre-print
However, recently a preprint saved on Arxiv claim that all research done for almost 20 years now on the Early Classification of Time Series is useless, or, at the very least, ill-oriented because severely  ...  The use of such data is explained by the fact that the first research works on ECTS first exploited methods based on distances or similarity measures, like K-Nearest Neighbor, etc.  ...  Conclusion and open challenges Early Classification of Time Series is definitely a meaningful problem for several reasons.  ... 
arXiv:2104.13257v2 fatcat:zhy7ctgtfjahxglatpzuzntwqy

Diffusion Autoencoders: Toward a Meaningful and Decodable Representation [article]

Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, Supasorn Suwajanakorn
2022 arXiv   pre-print
Our method can encode any image into a two-part latent code, where the first part is semantically meaningful and linear, and the second part captures stochastic details, allowing near-exact reconstruction  ...  Our key idea is to use a learnable encoder for discovering the high-level semantics, and a DPM as the decoder for modeling the remaining stochastic variations.  ...  To find nearest neighbors, we used three different metrics: 1) lowest LPIPS [61] in the image space, 2) lowest MSE in the image space, 3) lowest MSE in the semantic subspace (z sem ).  ... 
arXiv:2111.15640v3 fatcat:sahzuednxbb4dpjxswv6yilx2i

What is a meaningful representation of protein sequences? [article]

Nicki Skafte Detlefsen, Søren Hauberg, Wouter Boomsma
2022 arXiv   pre-print
This begs the question of what even constitutes the most meaningful representation.  ...  suboptimal performance, and in the latter we demonstrate that taking representation geometry into account significantly improves interpretability and lets the models reveal biological information that is  ...  In this graph each node will be a point in the grid, which is connected to its eight nearest neighbors, with the edge weight being the distance weighted with the entropy.  ... 
arXiv:2012.02679v4 fatcat:zv3j3pyicfaavdyltcahuucbrm

Implementing ReliefF filters to extract meaningful features from genetic lifetime datasets

Lorenzo Beretta, Alessandro Santaniello
2011 Journal of Biomedical Informatics  
Background: The analysis of survival data allows to evaluate whether in a population the genetic exposure is related to the time until an event occurs.  ...  Owing to the complexity of common human diseases, there is the incipient need to develop bioinformatics tools to properly model non-linear high-order interactions in lifetime datasets.  ...  The weighted Diff function is used also for calculating the distance between instances to find the nearest neighbors.  ... 
doi:10.1016/j.jbi.2010.12.003 pmid:21168527 fatcat:ge4lzjq5ybfjfjnlm6pviuwm6u
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