In biometric systems, reducing the data dimensionality without compromising intrinsic information is essential in pre-processing high-dimensional data. Many states of the art use techniques to minimize the dimensionality of such data and avoid the so-called curse of dimensionality. When operating on limited datasets, supervised methods suffer from over fitting. Reducing the semi-supervised dimensionality in the next comparison or classification module can affect the recognition efficiency. This article introduces a novel multi-view multimodal semi-supervised dimensionality reduction methodology that applies Multi-view Multidimensional scaling dimensionality reduction based on Gabor 2D-Log extraction features and Fuzzy Multiclass SVM classification (FMSVM), respectively. In addition, it examines its application to multi-view multimodal biometric processing, especially multi-view faces, and fingerprints. An experimental study was conducted, and the results emphasize that this methodology surpasses baseline supervised and semi-supervised methods.