带Boosting的半正定度量学习
Positive Semidefinite Metric Learning with Boosting
摘要：
学习合适的距离度量是图像分类和检索中的关键问题。在这项工作中，我们提出了一个基于提升的技术，称为\BoostMetric，学习马氏距离度量。学习这种度量的主要困难之一是确保Mahalanobis矩阵保持半正定。半定规划有时被用来强制执行这个约束，但不能很好地扩展。相反，\BoostMetric基于一个关键的观察，即任何半正定矩阵都可以分解为迹一秩一矩阵的线性正组合。因此，\BoostMetric使用秩一半正定矩阵作为有效和可扩展的基于增强的学习过程中的弱学习器。得到的方法易于实现，不需要调优，并且可以适应各种类型的约束。在不同数据集上的实验表明，该算法在分类精度和运行时间方面优于现有的方法。
英文摘要：
The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed \BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. \BoostMetric is instead based on a key observation that any positive semidefinite matrix can be decomposed into a linear positive combination of trace-one rank-one matrices. \BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting method is easy to implement, does not require tuning, and can accommodate various types of constraints. Experiments on various datasets show that the proposed algorithm compares favorably to those state-of-the-art methods in terms of classification accuracy and running time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
