  Manifold learning based methods have been widely used for non-linear
dimensionality reduction (NLDR). However, in many practical settings, the need
to process streaming data is a challenge for such methods, owing to the high
computational complexity involved. Moreover, most methods operate under the
assumption that the input data is sampled from a single manifold, embedded in a
high dimensional space. We propose a method for streaming NLDR when the
observed data is either sampled from multiple manifolds or irregularly sampled
from a single manifold. We show that existing NLDR methods, such as Isomap,
fail in such situations, primarily because they rely on smoothness and
continuity of the underlying manifold, which is violated in the scenarios
explored in this paper. However, the proposed algorithm is able to learn
effectively in presence of multiple, and potentially intersecting, manifolds,
while allowing for the input data to arrive as a massive stream.
