A fast fixed-point algorithm for independent component analysis
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Self-adaptive blind source separation based on activation functions adaptation
IEEE Transactions on Neural Networks
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In this paper, we study sparse representation of large-size natural scenes via local spatial dependency decomposition. We propose a local independent factorization model of natural scenes and develop a learning algorithm for adaptation of the synaptic weights. We investigate the dependency of neighboring location of the natural scene patches and derive learning algorithm to train the visual neural network. Some numerical experiments on natural scenes are performed to show the sparse representation of the visual sensory information.