Research And Application On The Dimensionality Reduction Algorithm With Neural Networks

One of the greatest challenge of machine learning is the curse of dimensionality. Tolessen this burden on the standard algorithms, a set of dimensionality reduction methods havebeen developed. Among them, the neural network based dimensionality reduction algorithms,which includes the sparse auto-encoder and deep neural networks, have shows their greatability and potentiality against complex real-data problems.In this paper, we first survey on the neural network based dimensionality reductiontechniques. In this part, a down-top investigation on the basic principle is first performed.Then, we analysis the interconnection between neural network based dimensionality reductionalgorithm and other state-of-art techniques, such as sparse coding and deep learning.Furthermore, a comparison between neural network and other dimensionality reductionmethods is done.We also adapted the neural network dimensionality reduction algorithm into TWO areaas application:FIRSTLY, on the aspect of Approximate Nearest Neighbour Searching, we developed aneural network based 2-phase nearest neighbour searching strategy, which is based on theconqure-and-divide idea and making full use of the hierarchical features learned by a neuralnetwork. Our algorithm makes it possible to achieve low dimensionality and considerablequality of features, bringing double improvement on accuracy and speed.SECONDLY, on the aspect of Iris Recognition, we proposed a fast framework for irisrecognition, which consists of a rotation invariant feature extraction module by PCET and afollowed dimensional reduction module implemented by a deep neural network.The experiment on both algorithms show that our adapted neural network can greatlyreduce the dimensionality whereas retaining high performance.