scholarly journals Distance Metric Facilitated Transportation between Heterogeneous Domains

2018 ◽  
Author(s):  
Han-Jia Ye ◽  
Xiang-Rong Sheng ◽  
De-Chuan Zhan ◽  
Peng He
Keyword(s):  
Linkage Relationship ◽  
Feature Sets ◽  
Cross Domain ◽  
Feature Spaces ◽  
Current Task ◽  
Label Information ◽  
Feature Difference ◽  
Training Examples ◽  
Asymmetric Transformation ◽  
World Image

Lacking training examples is one of the main obstacles to learning systems. Transfer learning aims to extract and utilize useful information from related datasets and assists the current task effectively. Most existing methods restrict tasks connection on the same feature sets, or require aligned examples cross domains, even cannot take full advantage of the limited label information. In this paper, we focus on transferring between heterogeneous domains, i.e., those with different feature spaces, and propose the Metric Transporation on HEterogeneous REpresentations (MapHere) approach. In particular, an asymmetric transformation map is first learned to compensate the  cross-domain feature difference based on linkage relationship between objects; then the inner-domain discrepancy is further reduced with learned optimal transportation. Note that both source domain and cross-domain relationship are fully utilized in MapHere, which helps improve target classification task a lot.  Experiments on synthetic dataset validate the importance of the ''metric facilitated'' consideration, while results on real-world image and text classification also show the superiority of the proposed MapHere approach.

scholarly journals Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 473
Author(s):  
Yongpeng Wang ◽  
Hong Yu ◽  
Guoyin Wang ◽  
Yongfang Xie
Keyword(s):  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Mapping Function ◽  
Linear Mapping ◽  
Feature Mapping ◽  
Target Domain ◽  
User Reviews ◽  
Cross Domain ◽  
Semantic Orientation ◽  
Label Information

Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

scholarly journals Multiple-View Active Learning for Environmental Sound Classification

2016 ◽  
Vol 12 (12) ◽  
pp. 49 ◽  
Author(s):  
Yan Zhang ◽  
Danjv Lv ◽  
Yili Zhao
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Environmental Sound ◽  
Single View ◽  
Feature Sets ◽  
Multiple View ◽  
Sound Classification ◽  
Training Examples ◽  
The Given ◽  
Sound Data

<p class="Abstract">Multi-view learning with multiple distinct feature sets is a rapid growing direction in machine learning with boosting the performance of supervised learning classification under the case of few labeled data. The paper proposes Multi-view Simple Disagreement Sampling (MV-SDS) and Multi-view Entropy Priority Sampling (MV-EPS) methods as the selecting samples strategies in active learning with multiple-view. For the given environmental sound data, the CELP features in 10 dimensions and the MFCC features in 13 dimensions are two views respectively. The experiments with a single view single classifier, SVML, MV-SDS and MV-EPS on the environmental sound extracted two of views, CELP &amp; MFCC are carried out to illustrate the results of the proposed methods and their performances are compared under different percent training examples. The experimental results show that multi-view active learning can effectively improve the performance of classification for environmental sound data, and MV-EPS method outperforms the MV-SDS.</p><div> </div>

scholarly journals Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation

2018 ◽  
Author(s):  
Yuguang Yan ◽  
Wen Li ◽  
Hanrui Wu ◽  
Huaqing Min ◽  
Mingkui Tan ◽  
...  
Keyword(s):  
Metric Spaces ◽  
Optimal Transport ◽  
Domain Adaptation ◽  
Learning Performance ◽  
Target Domain ◽  
Source Domain ◽  
Feature Spaces ◽  
Label Information ◽  
Real World Datasets ◽  
Heterogeneous Source

Heterogeneous domain adaptation (HDA) aims to exploit knowledge from a heterogeneous source domain to improve the learning performance in a target domain. Since the feature spaces of the source and target domains are different, the transferring of knowledge is extremely difficult. In this paper, we propose a novel semi-supervised algorithm for HDA by exploiting the theory of optimal transport (OT), a powerful tool originally designed for aligning two different distributions. To match the samples between heterogeneous domains, we propose to preserve the semantic consistency between heterogeneous domains by incorporating label information into the entropic Gromov-Wasserstein discrepancy, which is a metric in OT for different metric spaces, resulting in a new semi-supervised scheme. Via the new scheme, the target and transported source samples with the same label are enforced to follow similar distributions. Lastly, based on the Kullback-Leibler metric, we develop an efficient algorithm to optimize the resultant problem. Comprehensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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