检索结果-内蒙古大学图书馆

Learning a discriminant graph-based embedding with feature selection for image categorization

NEURAL NETWORKS 2019年 111卷 35-46页

作者： Zhu, Ruifeng Dornaika, Fadi Ruichek, Yassine Univ Bourgogne Franche Comte CNRS Lab Elect Informat & Image LE2i Belfort France Univ Basque Country UPV EHU Fac Comp Sci Leioa Spain Ikerbasque Basque Fdn Sci Bilbao Spain

graph-based embedding methods are very useful for reducing the dimension of high-dimensional data and for extracting their relevant features. In this paper, we introduce a novel nonlinear method called Flexible Discriminant graph-based embedding with feature selection (FDEFS). The proposed algorithm aims to classify image sample data in supervised learning and semi-supervised learning settings. Specifically, our method incorporates the Manifold Smoothness, Margin Discriminant embedding and the Sparse Regression for feature selection. The weights add l(2,1)-norm regularization for local linear approximation. The sparse regression implicitly performs feature selection on the original features of data matrix and of the linear transform. We also provide an effective solution method to optimize the objective function. We apply the algorithm on six public image datasets including scene, face and object datasets. These experiments demonstrate the effectiveness of the proposed embedding method. They also show that proposed the method compares favorably with many competing embedding methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： graph-based embedding Discriminant embedding Sparse regression Feature selection Semi-supervised learning Image categorization

来源：评论

学校读者我要写书评

暂无评论

Joint graph based embedding and feature weighting for image classification

引用

PATTERN RECOGNITION 2019年 93卷 458-469页

作者： Zhu, Ruifeng Dornaika, Fadi Ruichek, Yassine Univ Bourgogne Franche Comte CNRS Lab Elect Informat & Image LE2i Belfort France Univ Basque Country UPV EHU Fac Comp Sci San Sebastian Spain Ikerbasque Basque Fdn Sci Bilbao Spain

Recently, several inductive and flexible nonlinear data projection methods for graph-based semi supervised learning were proposed. These state-of-the art techniques have a good performance. However, they have not taken into account the relevance of the original features in their model estimation. In this paper, we propose a joint graph-based embedding and feature weighting for getting a flexible and inductive nonlinear data representation on manifolds. The proposed criterion explicitly estimates the feature weights together with the projected data and the linear transformation such that data smoothness and large margins are achieved in the projection space. Moreover, the paper introduces a kernel variant of the model in order to get an inductive nonlinear embedding that is close to a real nonlinear subspace for a good approximation of the embedded data. The proposed frameworks can be seamlessly used in semi supervised and supervised settings. The resulting optimization problems can be solved efficiently. The proposed embedding methods are evaluated on six public scene and face datasets. Experiments on image classification, in a semi-supervised setting, show that our proposed methods can have a performance that is better than that of many state-of-the-art methods including linear and nonlinear methods. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： graph-based embedding Discriminative embedding Feature weighting Supervised learning Semi-supervised learning Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Joint graph based embedding and Feature Weighting for Image Classification

Joint Graph Based Embedding and Feature Weighting for Image ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Zhu, Ruifeng Dornaika, Fadi Ruichek, Yassine Univ Bourgogne Franche Comte CNRS Lab Elect Informat & Image LE2I Belfort France Univ Basque Country Fac Comp Sci San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain

ISBN: (纸本)9781728119854

The graph-based embedding is an effective and useful method in reducing the dimension and extracting relevant data. This paper introduces a framework for classifying high dimensional data via a joint graph-based embedding and weighting method which could be used in semi-supervised or supervised learning. We design on effective optimization algorithm to solve the objective function. Experiments on image classification show that our proposed method can have a performance that is better than that of many state-of-the-art methods including linear and nonlinear methods.

关键词： graph-based embedding feature selection supervised learning semi-supervised learning image classification

来源：评论

学校读者我要写书评

暂无评论

Deep, Flexible Data embedding with graph-based Feature Propagation for Semi-supervised Classification

引用

COGNITIVE COMPUTATION 2023年第1期15卷 1-12页

作者： Dornaika, Fadi Ho Chi Minh City Open Univ 97 Vo Van TanDist 3 Ho Chi Minh City 70000 Vietnam

graph-based data representation has recently received much attention in the fields of machine learning and cognitive computation. Deep architectures and the semi-supervised learning paradigm are very closely related to natural cognitive systems. In this paper, and in the context of semi-supervised learning, we will be addressing deep graph-based data representation using a cascade of flexible embedding based on feature propagation over graphs. Inspired by connectionist models, we developed a deep architecture that performs data representation. In each layer, a graph is created over the current representation of the data. This graph is used to aggregate the current features of the input data and provide a layer-specific linear and non-linear representation. The semi-supervised scheme presented simultaneously satisfies several desired properties. These include graph-based regularization of the data structure - a geometrically motivated criterion, flexible non-linear projection (i.e., linear and non-linear projections are jointly estimated), graph-based feature propagation (providing a low-pass filter of the features in each layer), and deep architecture. Our work's main innovative aspect stems from the fact that each layer employs feature propagation (aggregation) before solving the layer-by-layer projection transformations. The proposed model can be learned layer by layer. In each layer, the non-linear data representation and linear regression are jointly estimated with a closed form solution. The proposed method was evaluated using semi-supervised classification tasks with six image datasets. These experiments demonstrated the effectiveness of the proposed approach, which can compete with a variety of competing semi-supervised methods. Compared to a flexible scheme for data representation, the introduced method improved the performance by 8.5% on average. Compared to a recent deep scheme for data representation, the introduced feature propagation improved the perfor

关键词： Semi-supervised learning graph-based embedding Manifold regularization graph construction Deep architecture Feature propagation

来源：评论

学校读者我要写书评

暂无评论

Elastic embedding through graph Convolution-based Regression for Semi-supervised Classification

引用

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2021年第4期15卷 56-56页

作者： Dornaika, F. Univ Basque Country UPV EHU Manuel Lardizabal 1 San Sebastian 20018 Spain IKERBASQUE Fdn Bilbao Spain

This article introduces a scheme for semi-supervised learning by estimating a flexible non-linear data representation that exploits Spectral graph Convolutions structure. Structured data are exploited in order to determine non-linear and linear models. The introduced scheme takes advantage of data-driven graphs at two levels. First, it incorporates manifold smoothness that is naturally encoded by the graph itself. Second, the regression model is built on the convolved data samples that are derived from the data and their associated graph. The proposed semi-supervised embedding can tackle the problem of over-fitting on neighborhood structures for image data. The proposed graph Convolution-based Semi-supervised embedding paves the way to new theoretical and application perspectives related to the non-linear embedding. Indeed, building flexible models that adopt convolved data samples can enhance both the data representation and the final performance of the learning system. Several experiments are conducted on six image datasets for comparing the introduced scheme with some state-of-the-art semi-supervised approaches. This empirical evaluation shows the effectiveness of the proposed embedding scheme.

关键词： Semi-supervised learning graph-based embedding elastic embedding graph convolution

来源：评论

学校读者我要写书评

暂无评论

Joint Label Propagation, graph and Latent Subspace Estimation for Semi-supervised Classification

引用

COGNITIVE COMPUTATION 2024年第3期16卷 827-840页

作者： Dornaika, Fadi Baradaaji, Abdullah Univ Basque Country UPV EHU San Sebastian Spain Ikerbasque Basque Fdn Sci Bilbao Spain

Obtaining labeled images and samples is a very expensive process and can require intensive labor. At the same time, there are often not enough labeled samples to train an effective classifier. graph-based semi-supervised methods have attracted much attention in the field because they can use both labeled and unlabeled data. In this letter, we suggest a novel graph-based semi-supervised learning approach that takes full advantage of a small set of labeled samples and a large set of unlabeled samples. We first explain the concept of graph-based semi-supervised learning. Our central proposal is to jointly estimate a low-rank graph together with a latent subspace and soft labels. The proposed system exploits the synergy between the graph information and the latent data representation. This extends and enriches the supervision information in the semi-supervised context and produces a good discriminative linear mapping between the original space and the latent subspace. Several experiments were performed with five image datasets using state-of-the-art methods. The results of the study reveal several noteworthy findings. The proposed framework generally outperforms recent competing approaches in estimating labels and linear embeddings, especially for large datasets such as MNIST. The superiority is maintained even for minimally labeled images, but the recognition accuracy does not always increase. However, in a few cases, the proposed method may be outperformed by other method when the number of labeled samples per class is only one (the dataset UMIST). When we set the number of labeled samples per class to 2, we find that the proposed method consistently performs better than the competing methods. For the extended Yale dataset, the accuracy of the proposed method is 67.08%. This is 1.68% higher than the accuracy of the nearest competing method and an impressive 30.08% higher than the accuracy of the farthest competing method. In the case of the FacePix dataset, the accura

关键词： Semi-supervised learning Discriminant embedding Soft labels graph construction graph-based embedding Image categorization Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Joint Label Inference and Discriminant embedding

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4413-4423页

作者： Dornaika, Fadi Baradaaji, Abdullah El Traboulsi, Youssof Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475001 Peoples R China Univ Basque Country Dept Comp Sci & Artificial Intelligence San Sebastian 20018 Spain Ikerbasque Basque Fdn Sci Bilbao 48009 Spain Lebanese Int Univ Dept Comp Sci Tripoli Lebanon

graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requirements of many emerging applications. However, in real-world applications, the scarcity of labeled data can negatively affect the performance of the semisupervised method. In this article, we present a new framework for semisupervised learning called joint label inference and discriminant embedding for soft label inference and linear feature extraction. The proposed criterion and its associated optimization algorithm take advantage of both labeled and unlabeled data samples in order to estimate the discriminant transformation. This type of criterion should allow learning more discriminant semisupervised models. Nine public image data sets are used in the experiments and method comparisons. These experimental results show that the performance of the proposed method is superior to that of many advanced semisupervised graph-based algorithms.

关键词： Data models Symmetric matrices Feature extraction Task analysis Optimization Manifolds Laplace equations Discriminant embedding graph-based embedding image categorization pattern recognition semisupervised learning (SSL) soft labels

来源：评论

学校读者我要写书评

暂无评论

A unified semi-supervised model with joint estimation of graph, soft labels and latent subspace

引用

NEURAL NETWORKS 2023年第1期166卷 248-259页

作者： Dornaika, Fadi Baradaaji, Abdullah Ho Chi Minh City Open Univ Ho Chi Minh City Vietnam Univ Basque Country UPV EHU San Sebastian Spain

Since manually labeling images is expensive and labor intensive, in practice we often do not have enough labeled images to train an effective classifier for the new image classification tasks. The graph-based SSL methods have received more attention in practice due to their convexity, scalability and efficiency. In this paper, we propose a novel graph-based semi-supervised learning method that takes full advantage of a small set of labeled graphs and a large set of unlabeled graph data. First, we explain the concept of graph-based semi-supervised learning. The core idea of these models is to jointly estimate a low-rank graph with soft labels and a latent subspace. The proposed scheme leverages the synergy between the graph structure and the data representation in terms of soft labels and latent features. This improves the monitoring information and leads to better discriminative linear transformation. Several experiments were conducted on five image datasets using state-of-the-art methods. These experiments show the effectiveness of the proposed semi-supervised method.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Semi-supervised learning Discriminant embedding Soft labels graph construction graph-based embedding Image categorization

来源：评论

学校读者我要写书评

暂无评论

Combining embedding-based and Semantic-based Models for Post-Hoc Explanations in Recommender Systems

Combining Embedding-Based and Semantic-Based Models for Post...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Le, Ngoc Luyen Abel, Marie-Helene Gouspillou, Philippe Cs 60319 CompiègneCedex 60203 France Vivocaz 8 B Rue de la Gare Mercin-et-Vaux02200 France

ISBN: (纸本)9798350337020

In today's data-rich environment, recommender systems play a crucial role in decision support systems. They provide to users personalized recommendations and explanations about these recommendations. embedding-based models, despite their widespread use, often suffer from a lack of interpretability, which can undermine trust and user engagement. This paper presents an approach that combines embedding-based and semantic-based models to generate post-hoc explanations in recommender systems, leveraging ontology-based knowledge graphs to improve interpretability and explainability. By organizing data within a structured framework, ontologies enable the modeling of intricate relationships between entities, which is essential for generating explanations. By combining embedding-based and semantic based models for post-hoc explanations in recommender systems, the framework we defined aims at producing meaningful and easy-to-understand explanations, enhancing user trust and satisfaction, and potentially promoting the adoption of recommender systems across the e-commerce sector. © 2023 IEEE.

关键词： graph-based embedding Ontology-based Knowledge graph Post-hoc Explanations Recommender System

来源：评论

学校读者我要写书评

暂无评论

Joint sparse graph and flexible embedding for graph-based semi-supervised learning

引用

NEURAL NETWORKS 2019年 114卷 91-95页

作者： Dornaika, F. El Traboulsi, Y. Univ Basque Country UPV EHU San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain

This letter introduces a framework for graph-based semi-supervised learning by estimating a flexible non-linear projection and its linear regression model. Unlike existing works, the proposed framework jointly estimates the graph structure, the non-linear projection, and the linear regression model. By adopting this joint estimation an overall optimality can be reached. A series of experiments are conducted on five image datasets in order to compare the proposed method with some state-of-art semi-supervised methods. This evaluation demonstrates the effectiveness of the proposed embedding method. These experiments show the superiority of the proposed framework over the joint estimation of the graph and soft labels. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： graph-based embedding Semi-supervised learning Non-linear projection graph construction Inductive model Discriminant embedding

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：