Abstract

Multi-label linear discriminant analysis (MLDA) has been explored for multi-label dimension reduction. However, MLDA involves dense matrices eigen-decomposition which is known to be computationally expensive for large-scale problems. In this paper, we show that the formulation of MLDA can be equivalently casted as a least squares problem so as to significantly reduce the computation burden and scale to the data collections with higher dimension. Further, it is also found that appealing regularization techniques can be incorporated into the least-squares model to boost generalization accuracy. Experimental results on several popular multi-label benchmarks not only verify the established equivalence relationship, but also demonstrate the effectiveness and efficiency of our proposed algorithms.

中文翻译：

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共享子空间最小二乘多标签线性判别分析

摘要

已经探索了多标签线性判别分析（MLDA）以减少多标签尺寸。但是，MLDA涉及密集矩阵的本征分解，这对于大型问题在计算上是昂贵的。在本文中，我们表明MLDA的公式可以等效地转换为最小二乘问题，从而显着减少计算负担并扩展到具有更高维度的数据集。此外，还发现可以将吸引人的正则化技术结合到最小二乘模型中以提高泛化精度。在几种流行的多标签基准测试中的实验结果不仅验证了已建立的等价关系，而且还证明了我们提出的算法的有效性和效率。