In this paper we propose a new wavelet transform applicable to functions
defined on graphs, high dimensional data and networks. The proposed method
generalizes the Haar-like transform proposed in \cite{gavish2010mwot}, and it
is similarly defined via a hierarchical tree, which is assumed to capture the
geometry and structure of the input data. It is applied to the data using a
multiscale filtering and decimation scheme, which can employ different wavelet
filters. We propose a tree construction method which results in efficient
representation of the input function in the transform domain.