PILAE: A Non-gradient Descent Learning Scheme for Deep Feedforward Neural Networks
This work addresses the need for more automated and efficient training algorithms in machine learning, particularly for deep neural networks, though it appears incremental as it builds on existing autoencoder methods.
The authors tackled the problem of training deep feedforward neural networks by proposing a non-gradient descent learning scheme called PILAE, which uses pseudoinverse learning and low-rank approximation to achieve better performance in terms of training efficiency and classification accuracy, with experimental results showing improved trade-offs.
In this work, a non-gradient descent learning (NGDL) scheme was proposed for deep feedforward neural networks (DNN). It is known that an autoencoder can be used as the building blocks of the multi-layer perceptron (MLP) DNN, the MLP is taken as an example to illustrate the proposed scheme of pseudoinverse learning algorithm for autoencoder (PILAE) in this paper. The PILAE with low rank approximation is a NGDL algorithm, and the encoder weight matrix is set to be the low rank approximation of the pseudoinverse of the input matrix, while the decoder weight matrix is calculated by the pseudoinverse learning algorithm. It is worth to note that only very few network structure hyper-parameters need to be tuned compared with classical gradient descent learning algorithm. Hence, the proposed algorithm could be regarded as a quasi-automated training algorithm which could be utilized in automated machine learning field. The experimental results show that the proposed learning scheme for DNN could achieve better performance on considering the tradeoff between training efficiency and classification accuracy.