Unsupervised hierarchical clustering using the learning dynamics of restricted Boltzmann machines

被引:3
|
作者
Decelle, Aurelien [1 ,2 ]
Seoane, Beatriz [1 ,2 ]
Rosset, Lorenzo [1 ]
机构
[1] Univ Complutense Madrid, Dept Fis Teor, Madrid 28040, Spain
[2] Univ Paris Saclay, INRIA Tau Team, CNRS, LISN, F-91190 Gif Sur Yvette, France
关键词
MEAN-FIELD THEORY; CONTACTS; PHASE;
D O I
10.1103/PhysRevE.108.014110
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
Data sets in the real world are often complex and to some degree hierarchical, with groups and subgroups of data sharing common characteristics at different levels of abstraction. Understanding and uncovering the hidden structure of these data sets is an important task that has many practical applications. To address this challenge, we present a general method for building relational data trees by exploiting the learning dynamics of the restricted Boltzmann machine. Our method is based on the mean-field approach, derived from the Plefka expansion, and developed in the context of disordered systems. It is designed to be easily interpretable. We tested our method in an artificially created hierarchical data set and on three different real-world data sets (images of digits, mutations in the human genome, and a homologous family of proteins). The method is able to automatically identify the hierarchical structure of the data. This could be useful in the study of homologous protein sequences, where the relationships between proteins are critical for understanding their function and evolution.
引用
收藏
页数:22
相关论文
共 50 条
  • [1] Spectral dynamics of learning in restricted Boltzmann machines
    Decelle, A.
    Fissore, G.
    Furtlehner, C.
    [J]. EPL, 2017, 119 (06)
  • [2] Mode-assisted unsupervised learning of restricted Boltzmann machines
    Haik Manukian
    Yan Ru Pei
    Sean R. B. Bearden
    Massimiliano Di Ventra
    [J]. Communications Physics, 3
  • [3] Mode-assisted unsupervised learning of restricted Boltzmann machines
    Manukian, Haik
    Pei, Yan Ru
    Bearden, Sean R. B.
    Di Ventra, Massimiliano
    [J]. COMMUNICATIONS PHYSICS, 2020, 3 (01)
  • [4] Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines
    Tramel, Eric W.
    Gabrie, Marylou
    Manoel, Andre
    Caltagirone, Francesco
    Krzakala, Florent
    [J]. PHYSICAL REVIEW X, 2018, 8 (04):
  • [5] Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics
    A. Decelle
    G. Fissore
    C. Furtlehner
    [J]. Journal of Statistical Physics, 2018, 172 : 1576 - 1608
  • [6] Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics
    Decelle, A.
    Fissore, G.
    Furtlehner, C.
    [J]. JOURNAL OF STATISTICAL PHYSICS, 2018, 172 (06) : 1576 - 1608
  • [7] UNSUPERVISED LEARNING FOR BOLTZMANN MACHINES
    DECO, G
    PARRA, L
    [J]. NETWORK-COMPUTATION IN NEURAL SYSTEMS, 1995, 6 (03) : 437 - 448
  • [8] Unsupervised Rotation Factorization in Restricted Boltzmann Machines
    Giuffrida, Mario Valerio
    Tsaftaris, Sotirios A.
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 (01) : 2166 - 2175
  • [9] LEARNING SPAM FEATURES USING RESTRICTED BOLTZMANN MACHINES
    da Silva, Luis Alexandre
    Pontara da Costa, Kelton Augusto
    Ribeiro, Patricia Bellin
    de Rosa, Gustavo Henrique
    Papa, Joao Paulo
    [J]. IADIS-INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS, 2016, 11 (01): : 99 - 114
  • [10] Unsupervised Synaptic Pruning Strategies for Restricted Boltzmann Machines
    Kalyan, Surabhi
    Joshi, Siddharth
    Sheik, Sadique
    Pedroni, Bruno U.
    Cauwenberghs, Gert
    [J]. 2018 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS): ADVANCED SYSTEMS FOR ENHANCING HUMAN HEALTH, 2018, : 447 - 450