A General Nonlinear Embedding Framework Based on Deep Neural Network
Recently there has been increasing interest in deep neural network due to its powerful represent ability in several successful applications such as speech recognition and image classification. In this paper, we propose a general nonlinear embedding framework based on deep neural network which can be utilized to implement a family of dimensionality reduction algorithms. The objective function of our framework consists of two terms: 1) an embedding term which transforms the input to a low-dimensional representation with a multilayer network, and 2) a regularization term which computes the reconstruction error of the original input by unrolling the multilayer network to a deep auto encoder. We adopt a layer-by-layer pretraining procedure to obtain good initial weights for the network, and then minimize the objective function by back propagating derivatives of the two terms. To evaluate the proposed framework, we perform face recognition and digit classification experiments. The experiments demonstrate that the proposed framework achieves better results than the state-of-the-art algorithms. The success of our framework further verifies deep neural network's advantages in representation learning.