In the context of deep learning with kernel machines, the deep Restricted Kernel Machine (DRKM) framework allows multiple levels of kernel PCA (KPCA) and Least-Squares Support Vector Machines (LSSVM) to be combined into a deep architecture using visible and hidden units. We propose a new method for DRKM classification coupling the objectives of KPCA and classification levels, with the hidden feature matrix lying on the Stiefel manifold. The classification level can be formulated as an LSSVM or as an MLP feature map, combining depth in terms of levels and layers. The classification level is expressed in its primal formulation, as the deep KPCA levels, in their dual formulation, can embed the most informative components of the data in a much lower dimensional space. The dual setting is independent of the dimension of the inputs and the primal setting is parametric, which makes the proposed method computationally efficient for both high-dimensional inputs and large datasets. In the experiments, we show that our developed algorithm can effectively learn from small datasets, while using less memory than the convolutional neural network (CNN) with high-dimensional data. and that models with multiple KPCA levels can outperform models with a single level. On the tested larger-scale datasets, DRKM is more energy efficient than CNN while maintaining comparable performance.

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