1 research outputs found
PILAE: A Non-gradient Descent Learning Scheme for Deep Feedforward Neural Networks
In this work, a non-gradient descent learning scheme is proposed for deep
feedforward neural networks (DNN). As we known, autoencoder can be used as the
building blocks of the multi-layer perceptron (MLP) deep neural network. So,
the MLP will be taken as an example to illustrate the proposed scheme of
pseudoinverse learning algorithm for autoencoder (PILAE) training. The PILAE
with low rank approximation is a non-gradient based learning 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 few network structure hyperparameters need to be tuned. Hence, the
proposed algorithm can be regarded as a quasi-automated training algorithm
which can be utilized in autonomous machine learning research field. The
experimental results show that the proposed learning scheme for DNN can achieve
better performance on considering the tradeoff between training efficiency and
classification accuracy.Comment: This work is our effort toward to realize AutoM