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Related Experiment Videos

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

Xingquan Zhu

    IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
    |June 30, 2011
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Cartesian Form for Vector Formulation01:26

    Cartesian Form for Vector Formulation

    The Cartesian form for vector formulation is a process to calculate  the moment of force using the position and force vectors. The moment of force is defined as the cross-product of these vectors, making it a vector quantity. The Cartesian form of the position and force vectors involves unit vectors, which can be used to express the cross-product in determinant form.

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    Formative Semi-Supervised Learning (fSSL) introduces a novel framework that leverages hidden concepts to improve semi-supervised learning. This approach overcomes limitations of primitive Semi-Supervised Learning (pSSL), excelling in both within-domain and cross-domain scenarios.

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Traditional Semi-Supervised Learning (SSL) methods, termed primitive Semi-Supervised Learning (pSSL), often incorporate unlabeled data via automated labeling.
    • pSSL faces challenges such as inaccurate labeling and an inability to effectively utilize out-of-domain samples.

    Purpose of the Study:

    • To introduce a formative Semi-Supervised Learning (fSSL) framework designed to enhance semi-supervised learning by exploring hidden features.
    • To address the limitations of pSSL, particularly its susceptibility to false labeling and difficulties with cross-domain data.

    Main Methods:

    • The fSSL framework models labeled and unlabeled samples as originating from underlying hidden concepts.
    • It focuses on recovering these hidden concepts to serve as novel features that bridge labeled and unlabeled data.

    Related Experiment Videos

  • Unlike pSSL, fSSL utilizes unlabeled data solely for feature generation, not direct inclusion in the training set.

    • Main Results:

    • fSSL demonstrates superior performance compared to pSSL-based methods.
    • The framework effectively overcomes the disadvantages inherent in traditional pSSL approaches.
    • Significant improvements were observed in both within-domain and cross-domain semi-supervised learning tasks.

    Conclusions:

    • The proposed fSSL framework offers a robust alternative to traditional pSSL methods.
    • fSSL's ability to extract meaningful hidden features enhances its applicability, especially for cross-domain learning.
    • This research highlights the potential of concept-based feature learning in advancing semi-supervised learning techniques.