Subspace method is classical pattern recognition method

that uses global information mainly to denote an image.Recently

with the introduction of deep learning

the feature extraction model based on local self-learning has attracted more and more attention.By using the theory of deep learning

this paper presents a new feature extraction model based on multi-layered deep local subspace sparse optimization to solve the problem of object recognition.Firstly

we calculate the PCA mapping matrix on the first layer by minimizing the reconstruction

error on the training sample set

then we optimize the feature mapping results through

L

1

norm to enhance the robustness of algorithm.Secondly

we use the output of the first layer as the input of second layer

then we implement same actions of feature learning.In this way we can map the image to deep PCA subspace.Finally we merge these feature extraction results from different layers with weighting and encode the merged feature with binary hash code and histogram segment code.After that

we obtain the multi-layered deep local subspace sparse feature.The experimental results on face database of FERET、AR、Yale and target database of MNIST、CIFAR-10 show that this feature extraction model can achieve high recognition rate and robustness for illumination

expression and pose.At the same time

compared with the convolutional neural networks

our algorithm owns the advantages of simple structure and fast convergent rate.