Kernel Recursive Least Squares Dictionary Learning Algorithm
This work addresses the computational efficiency challenge in online kernel dictionary learning for researchers and practitioners in signal processing and machine learning, representing an incremental improvement over existing methods.
The authors tackled the problem of online dictionary learning for kernel-based sparse representations by proposing a recursive least squares (RLS) update algorithm that works with single samples or mini-batches. The result showed that their method outperforms existing online kernel dictionary learning approaches and achieves classification accuracy close to batch-trained models while being significantly more efficient, as demonstrated on four datasets across different domains.
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.