Deterministic Bridge Regression for Compressive Classification
This work addresses the need for efficient classification in machine intelligence, but it appears incremental as it builds upon existing penalized error formulations with approximated norms.
The authors tackled the problem of pattern classification with compact representation by proposing an analytic bridge solution for compressive classification, which was validated on simulated and real-world data.
Pattern classification with compact representation is an important component in machine intelligence. In this work, an analytic bridge solution is proposed for compressive classification. The proposal has been based upon solving a penalized error formulation utilizing an approximated $\ell_p$-norm. The solution comes in a primal form for over-determined systems and in a dual form for under-determined systems. While the primal form is suitable for problems of low dimension with large data samples, the dual form is suitable for problems of high dimension but with a small number of data samples. The solution has also been extended for problems with multiple classification outputs. Numerical studies based on simulated and real-world data validated the effectiveness of the proposed solution.