응용통계연구 (The Korean Journal of Applied Statistics)

  • 제32권1호
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  • Pages.41-68
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  • 2019
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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멀티 뷰 기법 리뷰: 이해와 응용

Multi-view learning review: understanding methods and their application

  • 배강일 (중앙대학교 응용통계학과) ;
  • 이영섭 (동국대학교 통계학과) ;
  • 임창원 (중앙대학교 응용통계학과)
  • Bae, Kang Il (Department of Applied Statistics, Chung-Ang University) ;
  • Lee, Yung Seop (Department of Statistics, Dongguk University) ;
  • Lim, Changwon (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2018.11.06
  • 심사 : 2019.01.03
  • 발행 : 2019.02.28
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⟨ 이전 논문 다음 논문 ⟩

초록

멀티 뷰 기법은 데이터를 다양한 관점에서 보려는 접근 방법이며 데이터의 다양한 정보를 통합하여 사용하려는 시도이다. 최근 많은 연구가 진행되고 있는 멀티 뷰 기법에서는 단일 뷰 만을 이용하여 모형을 학습시켰을 때 보다 좋은 성과를 보인 경우가 많았다. 멀티 뷰 기법에서 딥 러닝 기법의 도입으로 이미지, 텍스트, 음성, 영상 등 다양한 분야에서 좋은 성과를 보였다. 본 연구에서는 멀티 뷰 기법이 인간 행동 인식, 의학, 정보 검색, 표정 인식 분야에서 직면한 여러 가지 문제들을 어떻게 해결하고 있는지 소개하였다. 또한 전통적인 멀티 뷰 기법들을 데이터 차원, 분류기 차원, 표현 간의 통합으로 분류하여 멀티 뷰 기법의 데이터 통합 원리를 리뷰 하였다. 마지막으로 딥 러닝 기법 중 가장 범용적으로 사용되고 있는 CNN, RNN, RBM, Autoencoder, GAN 등이 멀티 뷰 기법에 어떻게 응용되고 있는지를 살펴보았다. 이때 CNN, RNN 기반 학습 모형을 지도학습 기법으로, RBM, Autoencoder, GAN 기반 학습 모형을 비지도 학습 기법으로 분류하여 이 방법들이 대한 이해를 돕고자 하였다.

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.

키워드

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Figure 3.1. Illustration of consistency and complementary principles (Liu et al., 2015a).

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Figure 3.2. Data integration: projection method from each view onto common feature space.

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Figure 3.3. Flow chart of co-training learning.

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Figure 3.4. Two kinds of the multi-view strategy: (a) One-view-one-network strategy, (b) Multi-view-one-networkstrategy (Kang et al., 2017).

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Figure 4.1. The word-level matching CNN (Ma et al., 2015).

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Figure 5.1. A schematic of DCCA (Andrew et al., 2013).

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Figure 5.2. A schematic of DGCCA with deep networks for J views (Benton et al., 2017).

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Figure 5.3. A schematic of Multimodal DBM (Srivastava and Salakhutdinov, 2012).

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Figure 5.4. Autoencoder model (Jaques et al., 2017).

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Figure 5.5. Correspondence Autoencoder (Feng et al., 2014).

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Figure 5.7. Architecture of text-conditional convolutional GAN (Reed et al., 2016).

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Figure 4.2. (a) The simple RNN model (b) The m-RNN model (Mao et al., 2014).

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Figure 5.6. (a) Graphical model of JMVAE (b) Two approaches to estimate encoders with a single input, $q{\Phi}_{x}\;(z{\mid}x),\;q{\Phi}_{w}\;(z{\mid}x)$ on the JMVAE (Suzuki et al., 2016).

Table 6.1. R & Python Implementations of Multi-view Learning Models

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