  Dimensionality reduction (DR) of image features plays an important role in
image retrieval and classification tasks. Recently, two types of methods have
been proposed to improve the both the accuracy and efficiency for the
dimensionality reduction problem. One uses Non-negative matrix factorization
(NMF) to describe the image distribution on the space of base matrix. Another
one for dimension reduction trains a subspace projection matrix to project
original data space into some low-dimensional subspaces which have deep
architecture, so that the low-dimensional codes would be learned. At the same
time, the graph based similarity learning algorithm which tries to exploit
contextual information for improving the effectiveness of image rankings is
also proposed for image class and retrieval problem. In this paper, after above
two methods mentioned are utilized to reduce the high-dimensional features of
images respectively, we learn the graph based similarity for the image
classification problem. This paper compares the proposed approach with other
approaches on an image database.
