A novel dimension reduction and dictionary learning framework for high-dimensional data classification. (April 2021) - Li, Yanxia; Chai, Yi; Zhou, Han; Yin, Hongpeng
Highlights: A novel dimension reduction and dictionary learning framework is proposed. At dimension reduction stage, it learns a nonlinear mapping via an autoencoder. At dictionary learning stage, it preserves local structure and enhances class discrimination. The nonlinear mapping and dictionary are optimized jointly. It preserves nonlinear structure within data and results in enhanced classification performance. Abstract: High-dimensional problem poses significant challenges for dictionary learning based classification architecture. Joint Dimension Reduction and Dictionary Learning (JDRDL) framework shows great potential for overcoming the challenges caused by high dimensionality. However, most of the existing JDRDL approaches do not consider the complex nonlinear relationships within high-dimensional data, which limits their classification performance. To overcome this problem, a novel joint dimension reduction and dictionary learning framework is proposed in this paper for high-dimensional data classification. Firstly, at dimension reduction stage, an autoencoder is employed to learn a nonlinear mapping that reduces dimensionality and preserves nonlinear structure of the high-dimensional data. Then, at dictionary learning stage, the locality constraint with label embedding, which takes the locality and label information into account together, is incorporated into the learning process to preserve desirable nonlinear local structure and enhance class discrimination. Moreover, the mapping function and dictionary are optimized simultaneously to enhance the performance. Encouraging experimental results on multiple benchmark datasets confirm that the proposed framework is effective and efficient for high-dimensional data classification.