scholarly journals An Improved Hierarchical Datastructure for Nearest Neighbor Search

2019 ◽  
Vol 33 ◽  
pp. 10001-10002
Author(s):  
Mengdie Nie ◽  
Zhi-Jie Wang ◽  
Chunjing Gan ◽  
Zhe Quan ◽  
Bin Yao ◽  
...  
Keyword(s):  
High Dimension ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Nearest Neighbor Search ◽  
Computational Tool ◽  
Neighbor Search ◽  
Art Methods ◽  
Speed Up ◽  
Run Time

Nearest neighbor search is a fundamental computational tool and has wide applications. In past decades, many datastructures have been developed to speed up this operation. In this paper, we propose a novel hierarchical datastructure for nearest neighbor search in moderately high dimension. Our proposed method maintains good run time guarantees, and it outperforms several state-of-the-art methods in practice.

MP-RW-LSH

2021 ◽  
Vol 14 (13) ◽  
pp. 3267-3280
Author(s):  
Huayi Wang ◽  
Jingfan Meng ◽  
Long Gong ◽  
Jun Xu ◽  
Mitsunori Ogihara
Keyword(s):  
Nearest Neighbor ◽  
Edit Distance ◽  
State Of The Art ◽  
Hash Table ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Algorithmic Problem ◽  
Use Case ◽  
Hash Tables ◽  
Neighbor Search

Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-Sensitive Hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS- L 2 , a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in L 1 distance, and show that it achieves a better tradeoff between scalability and query efficiency than all existing LSH-based solutions. We also explain why a state-of-the-art ANNS -L 1 solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. Finally, as a use case, we construct, using MP-RW-LSH as the underlying "ANNS- L 1 engine", a new ANNS-E (E for edit distance) solution that beats the state of the art.

Speeding up probabilistic roadmap planners with locality-sensitive hashing

Robotica ◽  
2014 ◽  
Vol 33 (7) ◽  
pp. 1491-1506
Author(s):  
Mika T. Rantanen ◽  
Martti Juhola
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Exact Methods ◽  
Neighbor Search ◽  
Probabilistic Roadmap ◽  
Nearest Neighbor Searching ◽  
Major Bottleneck ◽  
Speed Up

SUMMARYA crucial part of probabilistic roadmap planners is the nearest neighbor search, which is typically done by exact methods. Unfortunately, searching the neighbors can become a major bottleneck for the performance. This can occur when the roadmap size grows especially in high-dimensional spaces. In this paper, we investigate how well the approximate nearest neighbor searching works with probabilistic roadmap planners. We propose a method that is based on the locality-sensitive hashing and show that it can speed up the construction of the roadmap considerably without reducing the quality of the produced roadmap.

scholarly journals Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i317-i325
Author(s):  
Spencer Krieger ◽  
John Kececioglu
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Nearest Neighbor ◽  
Secondary Structure Prediction ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Protein Secondary Structure ◽  
Nearest Neighbor Search ◽  
Protein Secondary Structure Prediction ◽  
Neighbor Search

Abstract Motivation Protein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy. Method We present a new hybrid approach to secondary structure prediction that gains the advantages of both template- and non-template-based methods. Our core template-based method is an algorithmic approach that uses metric-space nearest neighbor search over a template database of fixed-length amino acid words to determine estimated class-membership probabilities for each residue in the protein. These probabilities are then input to a dynamic programming algorithm that finds a physically valid maximum-likelihood prediction for the entire protein. Our hybrid approach exploits a novel accuracy estimator for our core method, which estimates the unknown true accuracy of its prediction, to discern when to switch between template- and non-template-based methods. Results On challenging CASP benchmarks, the resulting hybrid approach boosts the state-of-the-art Q8 accuracy by more than 2–10%, and Q3 accuracy by more than 1–3%, yielding the most accurate method currently available for both 3- and 8-state secondary structure prediction. Availability and implementation A preliminary implementation in a new tool we call Nnessy is available free for non-commercial use at http://nnessy.cs.arizona.edu.

scholarly journals Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings

2020 ◽  
Vol 8 ◽  
pp. 828-841
Author(s):  
Phillip Keung ◽  
Julian Salazar ◽  
Yichao Lu ◽  
Noah A. Smith
Keyword(s):  
Machine Translation ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Target Sentence ◽  
Nearest Neighbor Search ◽  
Parallel Corpora ◽  
Neighbor Search ◽  
Point Increase ◽  
Previous State ◽  
Parallel Text

We describe an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text. We use multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. We validate our technique by extracting parallel sentence pairs on the BUCC 2017 bitext mining task and observe up to a 24.5 point increase (absolute) in F1 scores over previous unsupervised methods. We then improve an XLM-based unsupervised neural MT system pre-trained on Wikipedia by supplementing it with pseudo-parallel text mined from the same corpus, boosting unsupervised translation performance by up to 3.5 BLEU on the WMT’14 French-English and WMT’16 German-English tasks and outperforming the previous state-of-the-art. Finally, we enrich the IWSLT’15 English-Vietnamese corpus with pseudo-parallel Wikipedia sentence pairs, yielding a 1.2 BLEU improvement on the low-resource MT task. We demonstrate that unsupervised bitext mining is an effective way of augmenting MT datasets and complements existing techniques like initializing with pre-trained contextual embeddings.

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

scholarly journals PCT: Point cloud transformer

2021 ◽  
Vol 7 (2) ◽  
pp. 187-199
Author(s):  
Meng-Hao Guo ◽  
Jun-Xiong Cai ◽  
Zheng-Ning Liu ◽  
Tai-Jiang Mu ◽  
Ralph R. Martin ◽  
...  
Keyword(s):  
Language Processing ◽  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
Nearest Neighbor Search ◽  
Local Context ◽  
Irregular Domain ◽  
Cloud Processing ◽  
Neighbor Search ◽  
Farthest Point

AbstractThe irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.

scholarly journals A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Sumedh Yadav ◽  
Mathis Bode
Keyword(s):  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Large Datasets ◽  
Test Results ◽  
Accuracy Test ◽  
Reasonable Proportion ◽  
Speed Up ◽  
Run Time ◽  
Information Graph

Abstract A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. The presented method consists of two approaches, one for reducing a given training set, and another for partitioning the selected/reduced set. The heuristic targets large datasets, since the primary goal is a significant reduction in training computation run-time without compromising prediction accuracy. Test results show that both approaches significantly speed-up the training task when compared against that of state-of-the-art shrinking heuristics available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy. A network design is also presented for a partitioning based distributed training formulation. Added speed-up in training run-time is observed when compared to that of serial implementation of the approaches.

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