Algorithm For Nearest Neighbor Search Research Articles

Research on Industry Question Answering System Based on Near Neighbor Vector Search

In the field of natural language processing, language-based question answering has been widely studied and made great achievements. As an important form of search engine, question answering system can further improve the search quality compared with traditional search engines. Traditional tree-based indexing methods are inefficient in high-dimensional space. This paper focuses on the application of k nearest neighbor search in large-scale image databases, and researches on high-dimensional indexing technology based on vector approximation method. Fast-Hessian detector is used to detect feature points and generate SURF feature description vector; Then, the initial matching point pair is obtained by fast approximate nearest neighbor search algorithm, and then the unidirectional matching results are matched bidirectionally; at the same time, it makes a detailed analysis of the situation that the events in the sentences are verbs of “abstract change”, so as to realize the automatic question and answer after the change of abstract relations between entities.

NSDH: A Nonlinear Supervised Discrete Hashing framework for large-scale cross-modal retrieval

Hashing technology has been widely used in approximate nearest neighbor search algorithms for large-scale cross-modal retrieval due to its significantly reduced storage and high-speed search capabilities. However, most existing supervised cross-modal hashing methods either mainly rely on binary pairwise similarity and fail to exploit the rich semantic information contained in the label matrix, or the use of single linear projections which suffer from limited information completeness. In this paper, we propose a novel method, named Nonlinear Supervised Discrete Hashing (NSDH). Specifically, NSDH consists of two components, (1) a semantic enhancement descriptor consisting of multiple linear projections that is used to extract comprehensive latent representations of heterogeneous multimedia data, which aligns the original heterogeneous features and integrates the rich semantic label matrix; (2) a fast discrete optimization module used to learn discriminative compact hash codes, which preserves the similarity information using an inner product between the real-valued embeddings of the output of the semantic enhancement descriptors. Therefore, NSDH leverages both the label matrix and similarity information in order to enhance the semantic information of the learned hash codes. In this way, the representation learning capability of the output layer of semantic enhancement descriptors can be greatly enhanced and as a result the learned hash codes are more discriminative. In addition, we present a fast discrete optimization algorithm to efficiently learn the binary hash codes. Results from our experiments on two benchmark datasets highlight the superiority of NSDH in comparison to many state-of-the-art cross-modal hashing methods.

Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm

Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm

How Good Are Modern Spatial Libraries?

Many applications today like Uber, Yelp, Tinder, etc. rely on spatial data or locations from its users. These applications and services either build their own spatial data management systems or rely on existing solutions. JTS Topology Suite (JTS), its C++ port GEOS, Google S2, ESRI Geometry API, and Java Spatial Index (JSI) are some of the spatial processing libraries that these systems build upon. These applications and services depend on indexing capabilities available in these libraries for high-performance spatial query processing. In this work, we compare these libraries qualitatively and quantitatively based on four different spatial queries using two real world datasets. We also compare these libraries with an open-source implementation of the Vantage Point Tree—an index structure that has been well studied in image retrieval and nearest-neighbor search algorithms for high-dimensional data. We found that Vantage Point Trees are very competitive and even outperform the aforementioned libraries in two queries.

VHP

Locality sensitive hashing (LSH) is a widely practiced c -approximate nearest neighbor( c -ANN) search algorithm in high dimensional spaces. The state-of-the-art LSH based algorithm searches an unbounded and irregular space to identify candidates, which jeopardizes the efficiency. To address this issue, we introduce the concept of virtual hypersphere partitioning. The core idea is to impose a virtual hypersphere, centered at the query, in the original feature space and only examine points inside the hypersphere. The search space of a hypersphere is isotropic and bounded, and thus more efficient than the existing one. In practice, we use multiple physical hyperspheres with different radii in corresponding projection subspaces to emulate the single virtual hypersphere. We also developed a principled method to compute the hypersphere radii for given success probability. Based on virtual hypersphere partitioning, we propose a novel disk-based indexing and searching scheme VHP to answer c -ANN queries. In the indexing phase, VHP stores LSH projections with independent B + -trees. To process a query, VHP keeps increasing the radii of physical hyperspheres co-ordinately, which in effect amounts to enlarging the virtual hypersphere, to accommodate more candidates until the success probability is met. Rigorous theoretical analysis shows that the proposed algorithm supports c -ANN search for arbitrarily small c ≥ 1 with probability guarantee. Extensive experiments on a variety of datasets, including the billion-scale ones, demonstrate that VHP could achieve different tradeoffs between efficiency and accuracy, and achieves up to 2x speedup in running time over the state-of-the-art methods.

A method for inspecting the cleanliness of spinneret holes

At present, computer vision system is widely used for the cleanliness inspection for spinneret holes, but it has a high misjudgment rate for many holes with small dirt. In this paper, a method is proposed to improve the accuracy of the cleanliness inspection. The method has four sequential phases. First, the closed contour curve of a standard hole and its curvature are extracted. Based on double-threshold segmentation of the contour curve, line and arc segments are segmented to generate a closed piecewise curve model. Second, the model is fitted to the closed contour curve L1 of the hole to be inspected based on a nonlinear least squares principle, and the distance curve that represents the shortest distance between the closed piecewise curve L2 constructed from the aligned model and L1 is calculated based on the nearest neighbor search algorithm. Third, the dirt detection curve L3 is generated from the distance curve weighted by a blended weighting curve. Final, based on a global threshold combining with unevenness elimination of L3, the cleanliness index is calculated based on the segmentation and location of dirt, and is used to judge whether the hole is qualified or not. The experimental results of four databases demonstrate that, the proposed method provides better performance compared with the traditional method.

Scalable Approximate FRNN-OWA Classification

Fuzzy rough nearest neighbor classification with ordered weighted averaging operators (FRNN-OWA) is an algorithm that classifies unseen instances according to their membership in the fuzzy upper and lower approximations of the decision classes. Previous research has shown that the use of OWA operators increases the robustness of this model. However, calculating membership in an approximation requires a nearest neighbor search. In practice, the query time complexity of exact nearest neighbor search algorithms in more than a handful of dimensions is near linear, which limits the scalability of FRNN-OWA. Therefore, we propose approximate FRNN-OWA, a modified model that calculates upper and lower approximations of decision classes using the approximate nearest neighbors returned by hierarchical navigable small worlds (HNSW), a recent approximative nearest neighbor search algorithm with logarithmic query time complexity at constant near-100% accuracy. We demonstrate that approximate FRNN-OWA is sufficiently robust to match the classification accuracy of exact FRNN-OWA while scaling much more efficiently. We test four parameter configurations of HNSW and evaluate their performance by measuring classification accuracy and construction and query times for samples of various sizes from three large datasets. We find that with two of the parameter configurations, approximate FRNN-OWA achieves near-identical accuracy to exact FRNN-OWA for most sample sizes within query times that are up to several orders of magnitude faster.

Vehicle Routing Problem with Multiple Time Windows and Batch Splitting Based on Inferior First Bidirectional Search Algorithm

Considering continuous scheduling and full use of time in vehicle routing problem, we proposed a vehicle routing problem model with multi-time windows based on batch splitting, constructed an inferior-first bidirectional search algorithm (IFBSA) and established a quantitative measurement benchmark of “inferiority level” using Gaussian kernel function. We designed 30 examples based on some actual road network data and chose nearest neighbour search algorithm (NNSA) for comparing. Our results showed that, IFBSA reduced vehicles in 20 examples and got superior results in 23 examples for all examples compared with NNSA.

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

Data about entities or objects associated with geographical or location information could be called as spatial data. Spatial data helps in identifying and positioning anyone or anything globally anywhere across the world. Instances of various spatial features that are closely found together are called as spatial co-located patterns. So far, the spatial co-located patterns have been used only for knowledge discovery process but it would serve a wide variety of applications if analyzed intensively. One such application is to use co-location pattern mining for a context aware based search. 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). For the above-said purpose, co-located nearest neighbor search algorithm namely “CONNEKT” is proposed. 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.

Complexity of Error-Correcting Code Based on Nearest Neighbor Search Algorithm

The Nearest Neighbor search algorithm considered in this paper is well known (Elias algorithm). It uses error-correcting codes and constructs appropriate hash-coding schemas. These schemas preprocess the data in the form of lists. Each list is contained in some sphere, centered at a code-word. The algorithm is considered for the cases of perfect codes, so the spheres and, consequently, the lists do not intersect. As such codes exist for the limited set of parameters, the algorithm is considered for some other generalizations of perfect codes, and then the same data point may be contained in different lists. A formula of time complexity of the algorithm is obtained for these cases, using coset weight structures of the mentioned codes

An unsupervised strategy for defending against multifarious reputation attacks

In electronic markets, malicious sellers often employ reviewers to carry out different types of attacks to improve their own reputations or destroy their opponents’ reputations. As such attacks may involve deception, collusion, and complex strategies, maintaining the robustness of reputation evaluation systems remains a challenging problem. From a platform manager’s view, no trader can be taken as a trustable benchmark for reference, therefore, accurate filtration of dishonest sellers and fraud reviewers and precise presentation of users’ reputations remains a challenging problem. Based on impression theory, this paper presents an unsupervised strategy, which first design a nearest neighbor search algorithm to select some typical lenient reviewers and strict reviewers. Then, based on these selected reviewers and the behavior expectation theory in impression theory, this paper adopts a classification algorithm that pre-classify sellers into honest and dishonest ones. Thirdly, another classification algorithm is designed to classify reviewers (i.e., buyers) into honest, dishonest, and uncertain ones according to their trading experiences with the pre-classified sellers. Finally, based on the ratings of various reviewers, this paper proposes a formula to estimate seller reputations. We further designed two general sets of experiments over simulated data and real data to evaluate our scheme, which demonstrate that our unsupervised scheme outperforms benchmark strategies in accurately estimating seller reputations. In particular, this strategy can robustly defend against various common attacks and unknown attacks.

A New Cell-Level Search Based Non-Exhaustive Approximate Nearest Neighbor (ANN) Search Algorithm in the Framework of Product Quantization

Non-exhaustive search is widely used in the approximate nearest neighbor (ANN) search. In this paper, we propose a new cell-level search-based non-exhaustive ANN search algorithm in the framework of product quantization (PQ) called cell-level PQ to speed up the ANN search. The cell-level search is introduced by searching all the PQ cells of the query vector on the cell level, and the length of the candidate list can be significantly reduced with negligible computational costs. Instead of using the high time-consuming coarse quantizers, which are necessary in all of the existing non-exhaustive ANN search algorithms such as inverted files (IVFs) and inverted multi-index (IMI), our proposed cell-level PQ reuses the PQ cells of query vector to reject database vectors so that the ANN search in the framework of PQ can be efficiently speeded up. In addition, because our proposed cell-level PQ does not need to store the auxiliary indexes of coarse quantizers for each database vector, no extra memory consumption is required. The experimental results on different databases demonstrate that our proposed cell-level PQ can significantly speed up the ANN search in the framework of PQ, and meanwhile, the search accuracy is almost the same as that of the standard PQ.

Capacitated Vehicle Routing Problems: Nearest Neighbour vs. Tabu Search

The purpose of the Vehicle Routing Problem is to obtain a vehicle route with a minimum mileage in meeting customer demand according to their respective locations. One variant of the Vehicle Routing Problem (VRP) is the Capacitated Vehicle Routing Problem (CVRP), namely VRP with vehicle capacity constraints. Problems with Capacitated Vehicle Routing Problems (CVRP), can be solved by using the nearest neighbour and Tabu Search Algorithm. The step to complete the Tabu Search Algorithm begins with the determination of the initial solution using Nearest Neighbour, determining alternative solutions with exchange, namely to move two points in the solution, evaluate alternative solutions with Tabu list, choose the best solution and set the optimum solution, update tabu list, then if the discharge criteria are met then the process stops and if not, then returns to the determination of alternative solutions. Based on the results of calculations using the Tabu Search Algorithm, the traveling distance is less about 10.01% than the nearest neighbor.

Research on feature point extraction and matching machine learning method based on light field imaging

At present, there are many methods to realize the matching of specified images with features, and the basic components include image feature point detection, feature description, and image matching. Based on this background, this article has done different research and exploration around these three aspects. The image feature point detection method is firstly studied, which commonly include image edge information-based feature detection method, corner information-based detection method, and various interest operators. However, all of the traditional detection methods are involved in problems of large computation burden and time consumption. In order to solve this problem, a feature detection method based on image grayscale information-FAST operator is used in this paper, which is combined with decision tree theory to effectively improve the speed of extracting image feature points. Then, the feature point description method BRIEF operator is studied, which is a local expression of detected image feature points based on descriptors. Since the descriptor does not have rotation invariance, the detection operator is endowed by a direction that is proposed in this paper, and then the local feature description is conducted on the feature descriptor to generate a binary string array containing direction information. Finally, the feature matching machine learning method is analyzed, and the nearest search method is used to find the nearest feature point pair in Euclidean distance, of which the calculation burden is small. The simulation results show that the proposed nearest neighbor search and matching machine learning algorithm has higher matching accuracy and faster calculation speed compared with the classical feature matching algorithm, which has great advantages in processing a large number of array images captured by the light field camera.

A probabilistic molecular fingerprint for big data settings

BackgroundAmong the various molecular fingerprints available to describe small organic molecules, extended connectivity fingerprint, up to four bonds (ECFP4) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (≥ 1024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality.ResultsHerein we report a new fingerprint, called MinHash fingerprint, up to six bonds (MHFP6), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. By leveraging locality sensitive hashing, LSH approximate nearest neighbor search methods perform as well on unfolded MHFP6 as comparable methods do on folded ECFP4 fingerprints in terms of speed and relative recovery rate, while operating in very sparse and high-dimensional binary chemical space.ConclusionMHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub (https://github.com/reymond-group/mhfp).

Surrogate Modeling of Manufacturing Variation Effects on Unsteady Interactions in a Transonic Turbine

This effort develops a surrogate modeling approach for predicting the effects of manufacturing variations on performance and unsteady loading of a transonic turbine. Computational fluid dynamics (CFD) results from a set of 105 as-manufactured turbine blade geometries are used to train and validate the surrogate models. Blade geometry variation is characterized with point clouds gathered from a structured light, optical measurement system and as-measured CFD grids are generated through mesh morphing of the nominal design grid data. Principal component analysis (PCA) of the measured airfoil geometry variations is used to create a reduced basis of independent surrogate model parameters. It is shown that the surrogate model typically captures between 60% and 80% of the CFD predicted variance. Three new approaches are introduced to improve surrogate effectiveness. First, a zonal PCA approach is defined which investigates surrogate accuracy when limiting analysis to key regions of the airfoil. Second, a training point reduction strategy is proposed that is based on the k–d tree nearest neighbor search algorithm and reduces the required training points up to 38% while only having a small impact on accuracy. Finally, an alternate reduction approach uses k-means clustering to effectively select training points and reduces the required training points up to 66% with a small impact on accuracy.

A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants

© 2018 Elsevier B.V. The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches.

Approximate nearest neighbor search for ℓp-spaces (2 < p < ∞) via embeddings

While the problem of approximate nearest neighbor search has been well-studied for Euclidean space and ℓ1, few non-trivial algorithms are known for ℓp when 2<p<∞. In this paper, we revisit this fundamental problem and present approximate nearest-neighbor search algorithms which give the best known approximation factor guarantees in this setting.

Capacity value of wind power using kd tree and nearest neighbor search algorithm

AbstractElectrical power demand is increasing cumulatively with the increase in population. Economic and environmental impacts restrict the expansion of conventional generation but prompting for intermittent wind sources due to its economic and environmental benefits. But the average capacity value of the wind power is not the same as that of the same rated conventional generation due to its intermittent nature and wind speed dependence. Several definitions of capacity value of wind power are proposed on the basis of reliability evaluation of the system. The estimation of capacity value is not easy as at hourly step wind speed is changing so the power. Therefore, a new procedure is developed here to accumulate all the information like the sequential value of wind power, their reliability indices, and the corresponding capacity values using the capacity value definitions in a single database. To search the capacity value of the wind power, the database is represented in a multidimensional k‐dimensional tree and nearest neighbor search algorithm is implemented. A case study is considered to validate the proposed methodology.

An Approximate Nearest Neighbour System For Neonatal EEG Recall.

Clinical neurophysiologists often find it difficult to recall rare EEG patterns despite the fact that this information could be diagnostic and help with treatment intervention. Traditional search methods may take time to retrieve the archived EEGs that could provide the meaning or cause of the specific pattern which is not acceptable as time can be critical for sick neonates. If neurophysiologists had the ability to quickly recall similar patterns, the prior occurrence of the pattern may help make an earlier diagnosis. This paper presents a system that may be used to assist a clinical neurophysiologist in the recall of neonatal EEG patterns. The proposed system consists of an alignment technique followed by an approximate nearest neighbour search algorithm called locality sensitive hashing. The system was tested on six different neonatal EEG pattern types with 430 events in total and the results are presented in this paper.