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.

Considering Relationship of Proteins for Radiotherapy Prognosis of Bladder Cancer Cells in Small Data Set

2018 ◽  
Vol 57 (04) ◽  
pp. 220-229
Author(s):  
Tung-I Tsai ◽  
Yaofeng Zhang ◽  
Gy-Yi Chao ◽  
Cheng-Chieh Tsai ◽  
Zhigang Zhang
Keyword(s):  
Bladder Cancer ◽  
Expression Profiles ◽  
Correlation Coefficients ◽  
Value Theory ◽  
Training Data ◽  
Percentage Error ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Protein Expression Profiles

Summary Background: Radiotherapy has serious side effects and thus requires prudent and cautious evaluation. However, obtaining protein expression profiles is expensive and timeconsuming, making it necessary to develop a theoretical and rational procedure for predicting the radiotherapy outcome for bladder cancer when working with limited data. Objective: A procedure for estimating the performance of radiotherapy is proposed in this research. The population domain (range of the population) of proteins and the relationships among proteins are considered to increase prediction accuracy. Methods: This research uses modified extreme value theory (MEVT), which is used to estimate the population domain of proteins, and correlation coefficients and prediction intervals to overcome the lack of knowledge regarding relationships among proteins. Results: When the size of the training data set was 5 samples, the mean absolute percentage error rate (MAPE) was 31.6200%; MAPE fell to 13.5505% when the number of samples was increased to 30. The standard deviation (SD) of forecasting error fell from 3.0609% for 5 samples to 1.2415% for 30 samples. These results show that the proposed procedure yields accurate and stable results, and is suitable for use with small data sets. Conclusions: The results show that considering the relationships among proteins is necessary when predicting the outcome of radiotherapy.

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.

HIERARCHICAL DISTANCE METRIC LEARNING FOR LARGE MARGIN NEAREST NEIGHBOR CLASSIFICATION

2011 ◽  
Vol 25 (07) ◽  
pp. 1073-1087 ◽  
Author(s):  
SHILIANG SUN ◽  
QIAONA CHEN
Keyword(s):  
Nearest Neighbor ◽  
Metric Learning ◽  
Data Sets ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Real World Data ◽  
K Nearest Neighbors ◽  
Large Margin ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

Distance metric learning is a powerful tool to improve performance in classification, clustering and regression tasks. Many techniques have been proposed for distance metric learning based on convex programming, kernel learning, dimension reduction and large margin. The recently proposed large margin nearest neighbor classification (LMNN) improves the performance of k-nearest neighbors classification (k-nn) by a learned global distance metric. However, it does not consider the locality of data distributions. We demonstrate a novel local distance metric learning method called hierarchical distance metric learning (HDM) which first builds a hierarchical structure by grouping data points according to the overlapping ratios defined by us and then learns distance metrics sequentially. In this paper, we combine HDM with LMNN and further propose a new method named hierarchical distance metric learning for large margin nearest neighbor classification (HLMNN). Experiments are performed on many artificial and real-world data sets. Comparisons with the traditional k-nn and the state-of-the-art LMNN show the effectiveness of the proposed HLMNN.

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 Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

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