Image clustering using exponential discriminant analysis

Local learning based image clustering models are usually employed to deal with images sampled from the non-linear manifold. Recently, linear discriminant analysis (LDA) based various clustering models were proposed. However, in these clustering models, regularisation parameter was added to handle the small-sample-size (SSS) problem of LDA for high-dimensional image data. Owing to this, the authors had to tune a number of clustering parameters for optimal clustering performance in existing local learning based clustering approaches. In this study, the less-parameterised local learning based image clustering model is proposed. The proposed local exponential discriminant clustering (LEDC) model is based on exponential discriminant analysis (EDA). In the LEDC model, local scatter matrices are projected in the exponential domain in order to handle the SSS problem of LDA without adding regularisation parameter. In the proposed LEDC model, k-nearest neighbours are the only clustering parameter as compared with existing local learning based clustering approaches such as normalised cut, spectral embedded clustering and local discriminant model and global integration (LDMGI). Experimental results on twelve benchmark image datasets show that the LEDC model achieved a comparable clustering performance as that of the near competitor LDMGI model. Clustering performance is comparable because no discriminant information of LDA is lost in EDA. The authors concluded that the proposed LEDC clustering model is less-parameterised with a comparable clustering performance as that of existing state-of-the-art local learning based clustering approaches.