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Multi-view predictive latent space learning

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Multi-view predictive latent space learning. / Yuan, Jirui; Gao, Ke; Zhu, Pengfei; Egiazarian, Karen.

In: Pattern Recognition Letters, 2018.

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Yuan, Jirui ; Gao, Ke ; Zhu, Pengfei ; Egiazarian, Karen. / Multi-view predictive latent space learning. In: Pattern Recognition Letters. 2018.

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@article{9cb1ca7270644eee998aafd677048836,
title = "Multi-view predictive latent space learning",
abstract = "In unsupervised circumstances, multi-view learning seeks a shared latent representation by taking the consensus and complementary principles into account. However, most existing multi-view unsupervised learning approaches do not explicitly lay stress on the predictability of the latent space. In this paper, we propose a novel multi-view predictive latent space learning (MVP) model and apply it to multi-view clustering and unsupervised dimension reduction. The latent space is forced to be predictive by maximizing the correlation between the latent space and feature space of each view. By learning a multi-view graph with adaptive view-weight learning, MVP effectively combines the complementary information from multi-view data. Experimental results on benchmark datasets show that MVP outperforms the state-of-the-art multi-view clustering and unsupervised dimension reduction algorithms.",
keywords = "Multi-view learning, Predictive latent space learning, Unsupervised clustering, Unsupervised dimension reduction",
author = "Jirui Yuan and Ke Gao and Pengfei Zhu and Karen Egiazarian",
year = "2018",
doi = "10.1016/j.patrec.2018.06.022",
language = "English",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
publisher = "Elsevier",

}

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TY - JOUR

T1 - Multi-view predictive latent space learning

AU - Yuan, Jirui

AU - Gao, Ke

AU - Zhu, Pengfei

AU - Egiazarian, Karen

PY - 2018

Y1 - 2018

N2 - In unsupervised circumstances, multi-view learning seeks a shared latent representation by taking the consensus and complementary principles into account. However, most existing multi-view unsupervised learning approaches do not explicitly lay stress on the predictability of the latent space. In this paper, we propose a novel multi-view predictive latent space learning (MVP) model and apply it to multi-view clustering and unsupervised dimension reduction. The latent space is forced to be predictive by maximizing the correlation between the latent space and feature space of each view. By learning a multi-view graph with adaptive view-weight learning, MVP effectively combines the complementary information from multi-view data. Experimental results on benchmark datasets show that MVP outperforms the state-of-the-art multi-view clustering and unsupervised dimension reduction algorithms.

AB - In unsupervised circumstances, multi-view learning seeks a shared latent representation by taking the consensus and complementary principles into account. However, most existing multi-view unsupervised learning approaches do not explicitly lay stress on the predictability of the latent space. In this paper, we propose a novel multi-view predictive latent space learning (MVP) model and apply it to multi-view clustering and unsupervised dimension reduction. The latent space is forced to be predictive by maximizing the correlation between the latent space and feature space of each view. By learning a multi-view graph with adaptive view-weight learning, MVP effectively combines the complementary information from multi-view data. Experimental results on benchmark datasets show that MVP outperforms the state-of-the-art multi-view clustering and unsupervised dimension reduction algorithms.

KW - Multi-view learning

KW - Predictive latent space learning

KW - Unsupervised clustering

KW - Unsupervised dimension reduction

U2 - 10.1016/j.patrec.2018.06.022

DO - 10.1016/j.patrec.2018.06.022

M3 - Article

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

ER -