Multiclass Adaptive Subspace Learning

In modern data analysis, there is an increasing trend towards the integration of information across diverse input formats and perspectives.If the available data is not given in large quantities deep learning is in general impractical.The recently introduced Adaptive Subspace Kernel Fusion (ASKF) technique provides an efficient solution for binary classification, facilitating the effective integration of diverse views throughout the learning process.In this paper, we extend ASKF by employing a vector-labeled multi-class model, eliminating the need for multiple individual models typically required in conventional one-vs-rest or one-vs-one approaches.We also evaluated the effect of using GPU-based numerical solvers, optimizing our problem formulation and the generated code for better efficiency.The approach is evaluated on various kernel functions, highlighting our methods ability of robustly dealing with multi-view data.