An alternative to SVM Method for Data Classification
This work addresses classification challenges for users dealing with high-dimensional or multi-class data, but it appears incremental as it builds on existing kernel methods.
The paper tackles the weaknesses of SVM in data classification, such as time processing and optimization failures, by proposing an alternative method based on minimum distance to optimal subspaces, achieving similar performance with sensitive improvements.
Support vector machine (SVM), is a popular kernel method for data classification that demonstrated its efficiency for a large range of practical applications. The method suffers, however, from some weaknesses including; time processing, risk of failure of the optimization process for high dimension cases, generalization to multi-classes, unbalanced classes, and dynamic classification. In this paper an alternative method is proposed having a similar performance, with a sensitive improvement of the aforementioned shortcomings. The new method is based on a minimum distance to optimal subspaces containing the mapped original classes.