A Target Recognition Method Based on Dirichlet Process Latent Variable Support Vector Machine Model

In target recognition community, when dealing with large-scale and complex distributed data, it is very expensive to train a classifier using all input data and the underlying structure of the data is ignored. To overcome these limitations, the Mixture-of-Experts (ME) system is proposed, which partitions the input data into several clusters and learns a classifier for each cluster. However, in the traditional ME system, the number of experts are fixed in advance and clustering procedure and the classification tasks are de-coupled. To deal with these problems, a Dirichlet Process mixture of Latent Variable Support Vector Machine (DPLVSVM) is proposed. In DPLVSVM model, the number of clusters is chosen automatically by DP mixture model, and the linear Latent Variable SVMs (LVSVM) are employed in each cluster. Different from previous algorithms, in DPLVSVM, the clustering procedure and LVSVM are jointly learned to gain infinite discriminative clusters. And the parameters can be inferred simply and effectively via Gibbs sampling technique. Based on the experimental data obtained from the synthesized dataset, Benchmark datasets and measured radar echo data, the effectiveness of proposed method is validated.