We present an algorithm for approximating a function defined over $d$-dimensional manifold utilizing only noisy values at locations sampled from the with noise. To produce approximation we do not require any knowledge regarding other than its dimension $d$. use Manifold Moving Least-Squares approach of (Sober and Levin 2016) to reconstruct atlas charts is built on-top those charts. The resultin...

Geometric iterative methods, including progressive approximation and geometric interpolation are efficient for fitting a given data set. With the development of big technology, number points has become massive, least-squares (LSPIA) is generally applied to fit mass data. Combining Schulz method calculating Moore–Penrose generalized inverse matrix with traditional LSPIA method, this paper presen...