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Related Experiment Video

Updated: Feb 2, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
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Integrative multi-view regression: Bridging group-sparse and low-rank models.

Gen Li1, Xiaokang Liu2, Kun Chen2

  • 1Department of Biostatistics, Columbia University, New York.

Biometrics
|November 21, 2018
PubMed
Summary

We introduce integrative reduced-rank regression (iRRR) for high-dimensional multi-view data. This method effectively identifies relevant views and predictors for improved predictive modeling.

Keywords:
composite penalizationgroup selectionintegrative multivariate analysismulti-view learningnuclear norm

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Multi-view data are common in science and engineering, often with high dimensionality.
  • Predicting multivariate responses from multi-view predictors presents challenges, especially when only a few views are relevant.

Purpose of the Study:

  • To develop a novel method, integrative reduced-rank regression (iRRR), for analyzing high-dimensional multi-view data.
  • To address the challenge where predictors within relevant views contribute collectively, not sparsely.

Main Methods:

  • Proposed integrative reduced-rank regression (iRRR) within a multivariate regression framework.
  • Employed a convex composite nuclear norm penalization for model estimation.
  • Developed an efficient algorithm using the alternating direction method of multipliers.

Main Results:

  • Derived non-asymptotic oracle bounds for iRRR under a restricted eigenvalue condition.
  • Demonstrated that iRRR unifies group-sparse and low-rank methods.
  • Showcased faster convergence rates compared to existing methods in multi-view learning settings.

Conclusions:

  • iRRR effectively extracts latent features from each view in a supervised manner.
  • The method is robust, with extensions for non-Gaussian and incomplete data.
  • Simulation studies and real-world data application confirm the efficacy of iRRR.