1 research outputs found
Image clustering using exponential discriminant analysis
Local learning based image clustering models are usually employed to deal with images sampled from the nonālinear manifold. Recently, linear discriminant analysis (LDA) based various clustering models were proposed. However, in these clustering models, regularisation parameter was added to handle the smallāsampleāsize (SSS) problem of LDA for highādimensional 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 lessāparameterised 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, kānearest 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 lessāparameterised with a comparable clustering performance as that of existing stateāofātheāart local learning based clustering approaches