A Bayesian Perspective on Locality Sensitive Hashing with Extensions for Kernel Methods

2015 ◽  
Vol 10 (2) ◽  
pp. 1-32 ◽  
Author(s):  
Aniket Chakrabarti ◽  
Venu Satuluri ◽  
Atreya Srivathsan ◽  
Srinivasan Parthasarathy
Keyword(s):  
Kernel Methods ◽  
Locality Sensitive Hashing

scholarly journals Kernelized Hashcode Representations for Relation Extraction

2019 ◽  
Vol 33 ◽  
pp. 6431-6440
Author(s):  
Sahil Garg ◽  
Aram Galstyan ◽  
Greg Ver Steeg ◽  
Irina Rish ◽  
Guillermo Cecchi ◽  
...  
Keyword(s):  
Kernel Methods ◽  
State Of The Art ◽  
Computational Cost ◽  
Relation Extraction ◽  
Locality Sensitive Hashing ◽  
Classification Methods ◽  
Classification Problems ◽  
Random Subspaces ◽  
Class Labels ◽  
Biomedical Relation Extraction

Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures. A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs. Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods. Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels. We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r.t. state-ofthe-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.

Subspace k-anonymity algorithm for location-privacy preservation based on locality-sensitive hashing

2019 ◽  
Vol 23 (5) ◽  
pp. 1167-1185
Author(s):  
Xiaohan Wang ◽  
Yonglong Luo ◽  
Shiyang Liu ◽  
Taochun Wang ◽  
Huihui Han
Keyword(s):  
Privacy Preservation ◽  
Location Privacy ◽  
Locality Sensitive Hashing

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

scholarly journals Kernel methods in system identification, machine learning and function estimation: A survey

Automatica ◽  
2014 ◽  
Vol 50 (3) ◽  
pp. 657-682 ◽  
Author(s):  
Gianluigi Pillonetto ◽  
Francesco Dinuzzo ◽  
Tianshi Chen ◽  
Giuseppe De Nicolao ◽  
Lennart Ljung
Keyword(s):  
Machine Learning ◽  
System Identification ◽  
Kernel Methods ◽  
Function Estimation ◽  
And Function

scholarly journals Trusted Player Transfer Evaluation for Sport Markets based on Blockchain and Locality-Sensitive Hashing

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chao Liu ◽  
Zengxi Li ◽  
Shunshun Liu ◽  
Jushi Xie ◽  
Chao Yan ◽  
...  
Keyword(s):  
Locality Sensitive Hashing

Superconvergent Nyström and degenerate kernel methods for eigenvalue problems

2011 ◽  
Vol 217 (20) ◽  
pp. 7851-7866 ◽  
Author(s):  
C. Alouch ◽  
P. Sablonnière ◽  
D. Sbibih ◽  
M. Tahrichi
Keyword(s):  
Kernel Methods ◽  
Eigenvalue Problems ◽  
Degenerate Kernel
Sign in / Sign up

Export Citation Format

Share Document