Kernel methods are powerful tools for analyzing data in various fields. However, they can be slow for large datasets. To address this, researchers developed a new method called CLAss-specific Subspace Kernel (CLASK) representation. This method uses class-specific kernel functions to create individual subspaces, improving efficiency and accuracy in classification tasks. By automatically selecting the best kernel functions for each class and using subset selection techniques, CLASK reduces complexity while maintaining high performance. Additionally, a parallel and sequential framework was introduced to handle large-scale learning tasks effectively.