MUNPE: Multi-view uncorrelated neighborhood preserving embedding for unsupervised feature extraction

In order to identify the shared subspace between two views, in Canonical Correlation Analysis (CCA), a popular multi-view dimension reduction technique tries to maximize correlation between the views. Although, there are frequently more than two views in many actual applications, it can only process data with two views. Earlier studies, data with more than two viewpoints were managed using either linear correlation or higher degree polynomial correlation. These two forms of correlation – pairwise and high-order – have distinct impacts on perspective consistency, with their effectiveness varying depending on the dataset. In some cases, both correlations can be beneficial, while in others, only one may be relevant. Therefore, leveraging both types of correlations is necessary to achieve flexible and comprehensive view consistency in data analysis. The link between multiview data viewed from diverse perspectives is established by these two types of correlation, that is linear correlation and high order correlation, each of which has a different effect on view consistency. In this paper, we propose a Multi-view Uncorrelated Neighborhood Preserving Embedding (MUNPE), which simultaneously considers two distinct types of correlation to give flexible view consistency. While keeping the local structures of each perspective, the MUNPE also takes into account the complementaries of numerous viewpoints. The MUNPE makes the characteristics gathered by numerous projections for each view uncorrelated in order to get many projections and reduce the duplication of low-dimensional data. Iterative methods are used to resolve the MUNPE, and the algorithm’s convergence has been demonstrated. The testing on the Multiple Features with real data sets were successful for MUNPE. It is observed that performance is better than CWMvEF, MULPP, MLLE, GCCA, DTCCA algorithms.