Unsupervised Subspace Learning via Analysis Dictionary Learning

The ubiquitous digit devices, sensors and social networks bring tremendous high-dimensional data. The high-dimensionality leads to high time complexity, large storage burden, and degradation of the generalization ability. Subspace learning is one of the most effective ways to eliminate the curse of dimensionality by projecting the data to a low-dimensional feature subspace. In this paper, we proposed a novel unsupervised feature dimension reduction method via analysis dictionary learning. By learning an analysis dictionary, we project a sample to a low-dimensional space and the feature dimension is the number of atoms in the dictionary. The coding coefficient vector is used as the low-dimensional representation of data because it reflects the distribution on the synthesis dictionary atoms. Manifold regularization is imposed on the low-dimensional representation of data to keep the locality of the original feature space. Experiments on four datasets show that the proposed unsupervised dimension reduction model outperforms the state-of-the-art methods.