使用粒度统计不变量进行分类的学习方法
Learning using granularity statistical invariants for classification
AI总结：
本文提出 LUGSI 学习范式，通过引入粒度统计不变量并最大化类间距离来缩小不变量矩阵，在增强数据结构信息的同时显著提升大规模数据集上的训练速度和泛化能力。
摘要：
使用统计不变量学习（LUSI）是一种新的学习范式，它采用弱收敛机制，可应用于更广泛的分类问题。然而，在训练过程中，对于大规模数据集，LUSI 中不变量矩阵的计算成本很高。为了解决这一问题，本文为 LUSI 引入了一种粒度统计不变量，并发展出一种新的学习范式，称为使用粒度统计不变量学习（LUGSI）。LUGSI 同时采用强收敛机制和弱收敛机制，从最小化期望风险的角度出发。据我们所知，这是首次构造粒度统计不变量。与 LUSI 相比，引入这种新的统计不变量带来两个优势。首先，它增强了数据的结构信息。其次，LUGSI 通过最大化类间距离，将大的不变量矩阵转化为较小的矩阵，从而实现了大规模数据集分类问题的可行性，并显著提高了模型运算的训练速度。实验结果表明，LUGSI 不仅表现出更好的泛化能力，而且展示了更快的训练速度，特别是在大规模数据集上。
英文摘要：
Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems. However, the computation cost of invariant matrices in LUSI is high for large-scale datasets during training. To settle this issue, this paper introduces a granularity statistical invariant for LUSI, and develops a new learning paradigm called learning using granularity statistical invariants (LUGSI). LUGSI employs both strong and weak convergence mechanisms, taking a perspective of minimizing expected risk. As far as we know, it is the first time to construct granularity statistical invariants. Compared to LUSI, the introduction of this new statistical invariant brings two advantages. Firstly, it enhances the structural information of the data. Secondly, LUGSI transforms a large invariant matrix into a smaller one by maximizing the distance between classes, achieving feasibility for large-scale datasets classification problems and significantly enhancing the training speed of model operations. Experimental results indicate that LUGSI not only exhibits improved generalization capabilities but also demonstrates faster training speed, particularly for large-scale datasets.
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。
