Deep self-organizing reservoir computing model for visual object recognition

Abstract

Reservoir computing becomes increasingly a hot spot in recent years. In this paper, we propose a deep self-organizing reservoir computing model for visual object recognition. First, through combination of Kohonen's self-organizing map and SHESN network, we present a self-organizing SHESN (SO-SHESN). In the new model, we adopt the same mechanism of generating reservoir as SHESN, but McCulloch-Pitts type reservoir neuron is replaced with radial basis function neuron. Correspondingly, unsupervised competitive learning is exploited to train both input weights and reservoir weights of SO-SHESN. Second, we propose a deep SO-SHESN model through a stack of well-trained reservoir layers. In such a stacked structure, a novel trial-and-readout learning algorithm is used for pre-training of layer-wise reservoir, in which each layer is trained independently from each other. Finally, the experimental results obtained on MNIST benchmark dataset show that our SO-SHESN achieves the test recognition error rate of 5.66%, which improves classical ESN and SHESN by 6.44% and 1.74%, respectively. Furthermore, the test error rate of our deep SO-SHESN could reach up to 1.39%, which outperforms SO-SHESN with single reservoir layer by 4.27% and approximately approaches the state-of-the-art result of 1% among existing traditional machine learning approaches with non-CNN features.