In many areas such as computational biology, finance or social sciences, knowledge of an underlying graph explaining the interactions between agents is paramount importance but still challenging. Considering that these may be based on nonlinear relationships adds further complexity to topology inference problem. Among latest methods respond this need a one proposed by authors, which estimates p...

Based on the theories of sliced inverse regression (SIR) and reproducing kernel Hilbert space (RKHS), a new approach RDSIR (RKHS-based Double SIR) to nonlinear dimension reduction for survival data is proposed. An isometric isomorphism constructed based RKHS property, then function in can be represented by inner product two elements that reside isomorphic feature space. Due censorship data, dou...

Oh et al ([25]) introduced the reproducing polynomial particle (RPP) shape functions that are piecewise polynomial and satisfy the Kronecker delta property. In this paper, we introduce RPPM (Reproducing Polynomial Particle Methods) that is the Galerkin approximation method associated with the use of the RPP approximation space. Planting particles in the computation domain in a patchwise uniform...

It has been known that Reproducing Kernel Particle (RKP) shape functions with Kronecker delta property are not available in simple forms. Thus, in this paper, we construct highly regular piecewise polynomial Reproducing Polynomial Particle (RPP) shape functions that satisfy the Kronecker Delta Property. Like RKP shape functions, the RPP shape functions reproduce the complete polynomials of degr...