Text

Hypergraph Learning : Methods and Practices

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation. In this paper, we first systematically review existing literature regarding hypergraph generation, including distance-based, representation-based, attribute-based, and network-based approaches. Then, we introduce the existing learning methods on a hypergraph, including transductive hypergraph learning, inductive hypergraph learning, hypergraph structure updating, and multi-modal hypergraph learning. After that, we present a tensor-based dynamic hypergraph representation and learning framework that can effectively describe high-order correlation in a hypergraph. To study the effectiveness and efficiency of hypergraph generation and learning methods, we conduct comprehensive evaluations on several typical applications, including object and action recognition, Microblog sentiment prediction, and clustering. In addition, we contribute a hypergraph learning development toolkit called THU-HyperG.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.5 May 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2548-2566

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CLASSIFICATION AND CLUSTERING
HYPERGRAPH LEARNING
HYPERGRAPH GENERATION
HYPERGRAPH LEARNING TOOL
TENSOR-BASED DYNAMIC HYPERGRAPH LEARNING

Info Detail Spesifik

DOI: 10.1109/TPAMI.2020.3039374

Pernyataan Tanggungjawab

Yue Gao, Zizhao Zhang, Haojie Lin, Xibin Zhao, Shaoyi Du, Changqing Zou

Versi lain/terkait

Lampiran Berkas

Komentar