Results 271 to 280 of about 10,866,611 (334)

Locality-Sensitive Hashing for Chi2 Distance

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
In the past 10 years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Nearest Neighbors search (or Approximate Nearest Neighbors search). If the Euclidean Locality Sensitive Hashing algorithm, which provides approximate nearest neighbors in a euclidean space with sublinear complexity, is probably ...
Gorisse, David, Cord, Matthieu, Precioso, Frédéric   +2 more
openaire   +2 more sources

An explicit study of local ozone budget and NOx-VOCs sensitivity in Shenzhen China

, 2020
In China, a significant reduction in primary pollution has been observed due to the Clean Air Action since 2013, and ozone pollution has become increasingly prominent over the past years.
Dan Yu, Z. Tan, K. Lu, Xuefei Ma, Xin Li, Shiyi Chen, Bo Zhu, Li-Liang Lin, Yanting Li, P. Qiu, Xinping Yang, Yuhan Liu, Haichao Wang, Ling-yan He, Xiaofeng Huang, Yuanhang Zhang   +15 more
semanticscholar   +1 more source

Locality sensitive hashing revisited

Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013
Locality Sensitive Hashing (LSH) is widely recognized as one of the most promising approaches to similarity search in high-dimensional spaces. Based on LSH, a considerable number of nearest neighbor search algorithms have been proposed in the past, with some of them having been used in many real-life applications. Apart from their demonstrated superior
Hongya Wang, Jiao Cao, LihChyun Shu, Davood Rafiei   +3 more
openaire   +1 more source

Distributed Locality Sensitivity Hashing

2010 7th IEEE Consumer Communications and Networking Conference, 2010
In this paper, we present DLSH Distributed Locality Sensitive Hashing, a similar-data search technology. The huge growth in the size of video content has broken the traditional multi-media index hosting and look-up solutions, these are not able to scale to the size of the current and projected index requirements.
Smita Wadhwa, Pawan Gupta
openaire   +1 more source

Fast locality-sensitive hashing

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011
Locality-sensitive hashing (LSH) is a basic primitive in several large-scale data processing applications, including nearest-neighbor search, de-duplication, clustering, etc. In this paper we propose a new and simple method to speed up the widely-used Euclidean realization of LSH.
Anirban Dasgupta, Ravi Kumar, Tamas Sarlos   +2 more
openaire   +1 more source

Local sensitivity analysis of failure possibility and its universal solution by fuzzy simulation

Structural And Multidisciplinary Optimization, 2021
Lu Wang, Guijie Li, Zhenzhou Lu, Kaixuan Feng   +3 more
semanticscholar   +1 more source

Dimensionality reduction for surrogate model construction for global sensitivity analysis: Comparison between active subspace and local sensitivity analysis

, 2021
Keli Lin, Zijun Zhou, C. Law, Bin Yang
semanticscholar   +1 more source

Metabisulfite Sensitivity and Local Dental Anesthesia

Survey of Anesthesiology, 1989
A case of sulfite sensitivity first manifesting as urticaria and acute airway obstruction following local anesthesia is described. A positive parenteral provocation test to metabisulfite was observed weeks after recovery of the patient from the clinical event.
H J, Schwartz, I A, Gilbert, K A, Lenner, T H, Sher, E R, McFadden   +4 more
openaire   +2 more sources

In Defense of Locality-Sensitive Hashing

IEEE Transactions on Neural Networks and Learning Systems, 2018
Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of ...
Kun Ding, Chunlei Huo, Bin Fan, Shiming Xiang, Chunhong Pan   +4 more
openaire   +2 more sources

Bit Reduction for Locality-Sensitive Hashing

IEEE Transactions on Neural Networks and Learning Systems
Locality-sensitive hashing (LSH) has gained ever-increasing popularity in similarity search for large-scale data. It has competitive search performance when the number of generated hash bits is large, reversely bringing adverse dilemmas for its wide applications.
Huawen Liu, Wenhua Zhou, Hong Zhang, Gang Li, Shichao Zhang, Xuelong Li   +5 more
openaire   +2 more sources