Speed Up Kernel Projection Vector Machine Using Kronecker Decomposition

We present a speedup algorithm for kernel projection vector machine (KPVM) based on kronecker product decomposition. The large scale kernel matrix K with size of n × n is factorized into two small matrices K₁ and K₂ with size n₁ × n₁ and n₂ × n₂ respectively where n₁ × n₂ = n. The time-consuming SVD operation on K in KPVM is calculated through K₁ and K₂. The computation complexity is reduced to O(n²) from O(n³) originally while generalization ability is undiminished or even better than KPVM.