  Isometric feature mapping (Isomap) is a promising manifold learning method.
However, Isomap fails to work on data which distribute on clusters in a single
manifold or manifolds. Many works have been done on extending Isomap to
multi-manifolds learning. In this paper, we first proposed a new
multi-manifolds learning algorithm (M-Isomap) with help of a general procedure.
The new algorithm preserves intra-manifold geodesics and multiple
inter-manifolds edges precisely. Compared with previous methods, this algorithm
can isometrically learn data distributed on several manifolds. Secondly, the
original multi-cluster manifold learning algorithm first proposed in
\cite{DCIsomap} and called D-C Isomap has been revised so that the revised D-C
Isomap can learn multi-manifolds data. Finally, the features and effectiveness
of the proposed multi-manifolds learning algorithms are demonstrated and
compared through experiments.

Machine Learning;Computer Vision and Pattern Recognition