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Multi-Target Regression via Robust Low-Rank Learning.

Xiantong Zhen, Mengyang Yu, Xiaofei He

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 4, 2017
    PubMed
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    Multi-layer Multi-target Regression (MMR) effectively models inter-target correlations and nonlinear relationships for multivariate prediction. This robust low-rank learning approach demonstrates superior performance across diverse real-world datasets.

    Area of Science:

    • Machine Learning
    • Data Mining
    • Computer Vision

    Background:

    • Multi-target regression is gaining popularity for its ability to learn multiple regression tasks simultaneously.
    • Challenges include jointly handling inter-target correlations and complex input-output relationships.
    • Existing methods struggle with intricate nonlinearities and structural dependencies.

    Purpose of the Study:

    • To propose a novel Multi-layer Multi-target Regression (MMR) framework.
    • To simultaneously model inter-target correlations and nonlinear input-output relationships.
    • To provide a general, flexible, and expressive multi-target regression paradigm.

    Main Methods:

    • MMR utilizes robust low-rank learning.
    • Explicitly encodes inter-target correlations using matrix elastic nets (MEN).

    Related Experiment Videos

  • Integrates kernel methods for nonlinear input-output relationships and an alternating optimization algorithm for efficient solving.

    • Main Results:

    • MMR leverages kernel methods for nonlinear feature learning and multi-layer architectures for correlation modeling.
    • Achieves consistently high performance across 18 diverse real-world datasets.
    • Outperforms representative state-of-the-art algorithms in multivariate prediction.

    Conclusions:

    • MMR offers a powerful new multi-layer learning paradigm for multi-target regression.
    • Demonstrates significant effectiveness and generality for complex prediction tasks.
    • Highlights the benefits of combining kernel methods and multi-layer learning for multivariate analysis.