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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression
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Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression

机译：通过监督流形正则化进行多输出回归的描述符学习

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摘要

Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.
机译：最近，多输出回归显示了解决计算机视觉和医学图像分析中难题的强大能力。但是，由于巨大的图像可变性和歧义性，从根本上挑战处理多输出回归的高度复杂的输入-目标关系，尤其是对于不加区分的高维表示。在本文中，我们提出了一种用于多输出回归的新型监督描述符学习（SDL）算法，该算法可以建立区分性和紧凑性特征表示，以提高多元估计性能。 SDL被公式化为具有监督流形正则化的矩阵的广义低阶近似。通过将原始特征转换为与目标对齐的新的低维空间，SDL能够同时提取与多变量目标密切相关的判别特征，并删除不相关和多余的信息。所实现的可区分但紧凑的描述符在很大程度上减少了多输出回归的可变性和歧义性，从而实现了更加准确和有效的多元估计。我们针对计算机视觉和医学图像分析的合成数据和现实世界多输出回归任务，对建议的SDL进行了广泛的评估。实验结果表明，所提出的SDL可以在所有任务上实现较高的多元估计精度，并且在性能上大大优于现有算法。我们的方法为多输出回归建立了一个新颖的SDL框架，可以广泛用于提高不同应用程序中的性能。

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