Distance metric learning for kernel density-based acoustic model under limited training data conditions

2015 ◽  
Author(s):  
Do Van Hai ◽  
Xiong Xiao ◽  
Eng Siong Chng ◽  
Haizhou Li
Keyword(s):  
Metric Learning ◽  
Kernel Density ◽  
Training Data ◽  
Acoustic Model ◽  
Distance Metric Learning ◽  
Distance Metric

scholarly journals Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning

2020 ◽  
Author(s):  
Elmira Yazdan ◽  
Sajjad Aghabozorgi Sahaf ◽  
Hamidreza Saligheh Rad
Keyword(s):  
Magnetic Resonance ◽  
Metric Learning ◽  
Tissue Response ◽  
Training Data ◽  
Distance Metric Learning ◽  
Learning Method ◽  
Distance Metric ◽  
Matching Algorithm ◽  
Correlation Algorithm ◽  
Space Data

Purpose: Magnetic Resonance Fingerprinting (MRF) is a novel framework that uses a random acquisition to acquire a unique tissue response, or fingerprint. Through a pattern-matching algorithm, every voxel-vise fingerprint is matched with a pre-calculated dictionary of simulated fingerprints to obtain MR parameters of interest. Currently, a correlation algorithm performs the MRF matching, which is time-consuming. Moreover, MRF suffers from highly undersampled k-space data, thereby reconstructed images have aliasing artifact, propagated to the estimated quantitative maps. We propose using a distance metric learning method as a matching algorithm and a Singular Value Decomposition (SVD) to compress the dictionary, intending to promote the accuracy of MRF and expedite the matching process. Materials and Methods: In this investigation, a distance metric learning method, called the Relevant Component Analysis (RCA) was used to match the fingerprints from the undersampled data with a compressed dictionary to create quantitative maps accurately and rapidly. An Inversion Recovery Fast Imaging with Steady-State (IR-FISP) MRF sequence was simulated based on an Extended Phase Graph (EPG) on a digital brain phantom. The performance of our work was compared with the original MRF paper. Results: Effectiveness of our method was evaluated with statistical analysis. Compared with the correlation algorithm and full-sized dictionary, this method acquires tissue parameter maps with more accuracy and better computational speed. Conclusion: Our numerical results show that learning a distance metric of the undersampled training data accompanied by a compressed dictionary improves the accuracy of the MRF matching and overcomes the computation complexity.

scholarly journals Safe Triplet Screening for Distance Metric Learning

2019 ◽  
Vol 31 (12) ◽  
pp. 2432-2491
Author(s):  
Tomoki Yoshida ◽  
Ichiro Takeuchi ◽  
Masayuki Karasuyama
Keyword(s):  
Optimization Problem ◽  
Metric Learning ◽  
Computational Cost ◽  
Training Data ◽  
Small Data ◽  
Data Sets ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Data Set ◽  
Optimal Distance

Distance metric learning has been widely used to obtain the optimal distance function based on the given training data. We focus on a triplet-based loss function, which imposes a penalty such that a pair of instances in the same class is closer than a pair in different classes. However, the number of possible triplets can be quite large even for a small data set, and this considerably increases the computational cost for metric optimization. In this letter, we propose safe triplet screening that identifies triplets that can be safely removed from the optimization problem without losing the optimality. In comparison with existing safe screening studies, triplet screening is particularly significant because of the huge number of possible triplets and the semidefinite constraint in the optimization problem. We demonstrate and verify the effectiveness of our screening rules by using several benchmark data sets.

scholarly journals Distance metric learning for graph structured data

2021 ◽  
Author(s):  
Tomoki Yoshida ◽  
Ichiro Takeuchi ◽  
Masayuki Karasuyama
Keyword(s):  
Metric Learning ◽  
Structured Data ◽  
Distance Metric Learning ◽  
Distance Metric

scholarly journals Chi-Squared Distance Metric Learning for Histogram Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Yang ◽  
Luhui Xu ◽  
Xiaopan Chen ◽  
Fengbin Zheng ◽  
Yang Liu
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Metric Learning ◽  
Nearest Neighbors ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Projected Gradient Method ◽  
Proper Distance ◽  
Chi Squared ◽  
Real World Datasets

Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. The chi-squared distance is a nonlinear metric and is widely used to compare histograms. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. With the iterative projected gradient method for optimization, we naturally introduce thel2,1norm regularization into the proposed method for sparse metric learning. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method.

Out-of-Distribution Detection Based on Distance Metric Learning

2020 ◽  
Author(s):  
Donghun Yang ◽  
Iksoo Shin ◽  
Mai Ngoc Kien ◽  
Hoyong Kim ◽  
Chanhee Yu ◽  
...  
Keyword(s):  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric
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