scholarly journals Deep Hierarchical Embedding for Simultaneous Modeling of GPCR Proteins in a Unified Metric Space

2021 ◽  
Author(s):  
Taeheon Lee ◽  
Sangseon Lee ◽  
Minji Kang ◽  
Sun Kim
Keyword(s):  
Motif Discovery ◽  
Metric Learning ◽  
Phylogenetic Reconstruction ◽  
Feature Space ◽  
Family Relation ◽  
Deep Feature ◽  
Hierarchical Relations ◽  
The Hierarchical Structure ◽  
Loss Term ◽  
Metric Distances

Abstract GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. However, modeling of GPCR families has been performed separately at each level. Relationships between GPCR proteins are ignored in these approaches as they process the information in the proteins with several disconnected models. In this study, we propose a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model. Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset. Metric distances in the deep feature space corresponded to the hierarchical family relation between GPCR proteins. Furthermore, we demonstrated that further downstream tasks, like phylogenetic reconstruction and motif discovery, are feasible in the constructed embedding space. These results show that hierarchical relations between sequences were successfully captured in both of technical and biological aspects.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

scholarly journals An Improved Metric Learning Approach for Degraded Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guofeng Zou ◽  
Yuanyuan Zhang ◽  
Kejun Wang ◽  
Shuming Jiang ◽  
Huisong Wan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Metric Learning ◽  
Computing Time ◽  
Feature Space ◽  
Projection Algorithm ◽  
Learning Approach ◽  
Learning Method ◽  
Matching Problem ◽  
Locality Preserving Projection ◽  
Locality Preserving

To solve the matching problem of the elements in different data collections, an improved coupled metric learning approach is proposed. First, we improved the supervised locality preserving projection algorithm and added the within-class and between-class information of the improved algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. Furthermore, we extended this algorithm to nonlinear space, and the kernel coupled metric learning method based on supervised locality preserving projection is proposed. In kernel coupled metric learning approach, two elements of different collections are mapped to the unified high dimensional feature space by kernel function, and then generalized metric learning is performed in this space. Experiments based on Yale and CAS-PEAL-R1 face databases demonstrate that the proposed kernel coupled approach performs better in low-resolution and fuzzy face recognition and can reduce the computing time; it is an effective metric method.

scholarly journals Deep Coupling Recurrent Auto-Encoder with Multi-Modal EEG and EOG for Vigilance Estimation

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1316
Author(s):  
Kuiyong Song ◽  
Lianke Zhou ◽  
Hongbin Wang
Keyword(s):  
Data Analysis ◽  
Traffic Safety ◽  
Feature Fusion ◽  
Pearson Correlation ◽  
Metric Learning ◽  
Feature Space ◽  
Research Field ◽  
Function Optimization ◽  
Analysis Model ◽  
New Feature

Vigilance estimation of drivers is a hot research field of current traffic safety. Wearable devices can monitor information regarding the driver’s state in real time, which is then analyzed by a data analysis model to provide an estimation of vigilance. The accuracy of the data analysis model directly affects the effect of vigilance estimation. In this paper, we propose a deep coupling recurrent auto-encoder (DCRA) that combines electroencephalography (EEG) and electrooculography (EOG). This model uses a coupling layer to connect two single-modal auto-encoders to construct a joint objective loss function optimization model, which consists of single-modal loss and multi-modal loss. The single-modal loss is measured by Euclidean distance, and the multi-modal loss is measured by a Mahalanobis distance of metric learning, which can effectively reflect the distance between different modal data so that the distance between different modes can be described more accurately in the new feature space based on the metric matrix. In order to ensure gradient stability in the long sequence learning process, a multi-layer gated recurrent unit (GRU) auto-encoder model was adopted. The DCRA integrates data feature extraction and feature fusion. Relevant comparative experiments show that the DCRA is better than the single-modal method and the latest multi-modal fusion. The DCRA has a lower root mean square error (RMSE) and a higher Pearson correlation coefficient (PCC).

scholarly journals Deep Feature Space: A Geometrical Perspective

2021 ◽  
pp. 1-1
Author(s):  
Ioannis Kansizoglou ◽  
Loukas Bampis ◽  
Antonios Gasteratos
Keyword(s):  
Feature Space ◽  
Deep Feature

scholarly journals Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

2020 ◽  
Vol 34 (04) ◽  
pp. 6243-6250 ◽  
Author(s):  
Qian Wang ◽  
Toby Breckon
Keyword(s):  
Domain Adaptation ◽  
Feature Space ◽  
Structured Prediction ◽  
Target Domain ◽  
Source Domain ◽  
Domain Specific ◽  
Unsupervised Domain Adaptation ◽  
Deep Feature ◽  
Significant Performance ◽  
Error Accumulation

Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains. As a result, classifiers trained from labeled samples in the source domain suffer from significant performance drop when directly applied to the samples from the target domain. To address this issue, different approaches have been proposed to learn domain-invariant features or domain-specific classifiers. In either case, the lack of labeled samples in the target domain can be an issue which is usually overcome by pseudo-labeling. Inaccurate pseudo-labeling, however, could result in catastrophic error accumulation during learning. In this paper, we propose a novel selective pseudo-labeling strategy based on structured prediction. The idea of structured prediction is inspired by the fact that samples in the target domain are well clustered within the deep feature space so that unsupervised clustering analysis can be used to facilitate accurate pseudo-labeling. Experimental results on four datasets (i.e. Office-Caltech, Office31, ImageCLEF-DA and Office-Home) validate our approach outperforms contemporary state-of-the-art methods.

scholarly journals Variable star classification using multiview metric learning

2019 ◽  
Vol 491 (3) ◽  
pp. 3805-3819 ◽  
Author(s):  
K B Johnston ◽  
S M Caballero-Nieves ◽  
V Petit ◽  
A M Peter ◽  
R Haber
Keyword(s):  
Machine Learning ◽  
Variable Star ◽  
Metric Learning ◽  
Feature Space ◽  
Variable Stars ◽  
Machine Learning Techniques ◽  
Feature Representations ◽  
Learning Framework ◽  
Multiview Learning ◽  
The Matrix

ABSTRACT Comprehensive observations of variable stars can include time domain photometry in a multitude of filters, spectroscopy, estimates of colour (e.g. U-B), etc. When the objective is to classify variable stars, traditional machine learning techniques distill these various representations (or views) into a single feature vector and attempt to discriminate among desired categories. In this work, we propose an alternative approach that inherently leverages multiple views of the same variable star. Our multiview metric learning framework enables robust characterization of star categories by directly learning to discriminate in a multifaceted feature space, thus, eliminating the need to combine feature representations prior to fitting the machine learning model. We also demonstrate how to extend standard multiview learning, which employs multiple vectorized views, to the matrix-variate case which allows very novel variable star signature representations. The performance of our proposed methods is evaluated on the UCR Starlight and LINEAR data sets. Both the vector and matrix-variate versions of our multiview learning framework perform favourably – demonstrating the ability to discriminate variable star categories.

scholarly journals A Semi-Supervised Metric Learning for Content-Based Image Retrieval

2011 ◽  
Vol 1 (3) ◽  
pp. 53-63 ◽  
Author(s):  
I. Daoudi ◽  
K. Idrissi
Keyword(s):  
Probability Distributions ◽  
Metric Learning ◽  
Feature Space ◽  
Global Constraints ◽  
Learning Methods ◽  
Class A ◽  
Kernel Distance ◽  
New Space ◽  
Class Probability ◽  
Original Feature

In this paper, the authors propose a kernel-based approach to improve the retrieval performances of CBIR systems by learning a distance metric based on class probability distributions. Unlike other metric learning methods which are based on local or global constraints, the proposed method learns for each class a nonlinear kernel which transforms the original feature space to a more effective one. The distances between query and database images are then measured in the new space. Experimental results show that the kernel-based approach not only improves the retrieval performances of kernel distance without learning, but also outperforms other kernel metric learning methods.

scholarly journals Tag-Aware Personalized Recommendation Using a Hybrid Deep Model

2017 ◽  
Author(s):  
Zhenghua Xu ◽  
Thomas Lukasiewicz ◽  
Cheng Chen ◽  
Yishu Miao ◽  
Xiangwu Meng
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Feature Space ◽  
Personalized Recommendation ◽  
Neural Network Approach ◽  
Deep Model ◽  
Deep Feature ◽  
Training Quality ◽  
Training Efficiency ◽  
Better Than

Recently, many efforts have been put into tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). To ensure the scalability in practice, we further propose to improve this model's training efficiency by using hybrid deep learning and negative sampling. Experimental results show that our approach can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation (3.8 times better than the best baseline), and that using hybrid deep learning and negative sampling can dramatically enhance the model's training efficiency (hundreds of times quicker), while maintaining similar (and sometimes even better) training quality and recommendation performance.

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