Tensor-based embedding for graph-based semi-supervised approaches

被引：3

作者：

Ioannis, Georgoulas ^{[1
]}

Eftychios, Protopapadakis ^{[2
]}

Konstantinos, Makantasis ^{[3
]}

Anastasios, Doulamis ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Athens, Greece

[2] Univ Macedonia, Thessaloniki, Greece

[3] Univ Malta, Msida, Malta

来源：

PROCEEDINGS OF THE 16TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS, PETRA 2023 | 2023年

基金：

欧盟地平线“2020”;

关键词：

tensor-based embedding; graph-based; semi-supervised learning; hyperspectral data; CLASSIFICATION;

D O I：

10.1145/3594806.3596550

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a novel approach to multiclass classification tasks, utilizing tensor-based embeddings for graph-based semi-supervised learning. The proposed method utilizes a tensor decomposition algorithm to create embeddings that capture the essential features of the data. These are used by various graph-based semi-supervised approach to construct a graph capable to propagate the information from labeled to unlabeled nodes, classifying available data. The proposed method was tested on hyperspectral datasets. The results demonstrate the potential of such combinatory tensor-based semi-supervised approaches.

引用

页码：632 / 637

页数：6

共 18 条

[11] Tensor-Based Classification Models for Hyperspectral Data Analysis [J].