  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.
