提出一种高效在线字典学习算法，实现核方法下的稀疏表示。
Kernel Recursive Least Squares Dictionary Learning Algorithm
- 基于递归最小二乘法，支持单样本或小批量更新字典
- 在四个数据集上分类精度接近离线训练模型，效率显著更高
- 适用于实时处理场景，兼顾精度与计算开销
我们提出一种高效的在线字典学习算法，用于基于核的稀疏表示。在此框架中，输入信号被非线性映射到高维特征空间，并使用虚拟字典进行稀疏表示。每一步均通过基于递归最小二乘（RLS）方法的新算法递归更新字典。该更新机制支持单样本或小批量处理，且保持低计算复杂度。在四个跨领域的数据集上的实验表明，该方法不仅优于现有在线核字典学习方法，还能达到接近离线训练模型的分类精度，同时显著更高效。
原文摘要 · Abstract (English)
We propose an efficient online dictionary learning algorithm for kernel-based sparse representations. In this framework, input signals are nonlinearly mapped to a high-dimensional feature space and represented sparsely using a virtual dictionary. At each step, the dictionary is updated recursively using a novel algorithm based on the recursive least squares (RLS) method. This update mechanism works with single samples or mini-batches and maintains low computational complexity. Experiments on four datasets across different domains show that our method not only outperforms existing online kernel dictionary learning approaches but also achieves classification accuracy close to that of batch-trained models, while remaining significantly more efficient.
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