Power of kernel functions, its benefits, and limitations

In today's realm of machine learning, the non-linearity of data is the problem often faced during data analysis. A well-known supervised learning algorithm that is known to efficiently handle this problem is the Support Vector Machine. The trick deployed in the background to overcome the problem of non-linearity is the transformation of data from the existing dimension to a higher dimension. To make this conversion of dimensions possible, kernel functions are utilized. These kernel functions are simply mathematical functions, which are available in the literature for a long ago. The present study briefly describes the mathematical background of the kernel functions with examples and a quick summarization of the recent applications of the renowned kernel functions across various domains. Further, the numerous benefits, a few limitations of kernel functions, and future directions are also highlighted in the work.