| Abstract: | Nonnegative matrix factorization(NMF) is an effective dimension reduction method, which is widely used in image clustering and other fields. Some NMF variants preserve the manifold structure of the original data. However, the construction of the traditional neighbor graph depends on the original data, so it may be affected by noise and outliers. Moreover, these methods are unsupervised and do not use available label information. Therefore, this paper presents an adaptive graph-based discriminative nonnegative matrix factorization(AGDNMF). AGDNMF uses the available label to construct the label matrix, such that the new representations with the same label data are aligned to the same axis. And the neighbor graph in AGDNMF is obtained by adaptive iterations. A number of experiments on many image data sets verify that AGDNMF is effective compared with the other state-of-the-art methods. |
