Diffusion operators for multimodal data analysis

Tal Shnitzer, Roy R. Lederman, Gi-Ren Liu, Ronen Talmon, Hau Tieng Wu

研究成果: Chapter

摘要

In this chapter, we present a Manifold Learning viewpoint on the analysis of data arising from multiple modalities. We assume that the high-dimensional multimodal data lie on underlying low-dimensional manifolds and devise a new data-driven representation that accommodates this inherent structure. Based on diffusion geometry, we present three composite operators, facilitating different aspects of fusion of information from different modalities in different settings. These operators are shown to recover the common structures and the differences between modalities in terms of their intrinsic geometry and allow for the construction of data-driven representations which capture these characteristics. The properties of these operators are demonstrated in four applications: recovery of the common variable in two camera views, shape analysis, foetal heart rate identification and sleep dynamics assessment.

原文English
主出版物標題Handbook of Numerical Analysis
編輯Ron Kimmel, Xue-Cheng Tai
發行者Elsevier B.V.
頁面1-39
頁數39
ISBN(列印)9780444641403
DOIs
出版狀態Published - 2019 一月 1

出版系列

名字Handbook of Numerical Analysis
20
ISSN(列印)1570-8659

All Science Journal Classification (ASJC) codes

  • Numerical Analysis
  • Modelling and Simulation
  • Computational Mathematics
  • Applied Mathematics

指紋 深入研究「Diffusion operators for multimodal data analysis」主題。共同形成了獨特的指紋。

  • 引用此

    Shnitzer, T., Lederman, R. R., Liu, G-R., Talmon, R., & Wu, H. T. (2019). Diffusion operators for multimodal data analysis. 於 R. Kimmel, & X-C. Tai (編輯), Handbook of Numerical Analysis (頁 1-39). (Handbook of Numerical Analysis; 卷 20). Elsevier B.V.. https://doi.org/10.1016/bs.hna.2019.07.008