  In topological data analysis, a point cloud data P extracted from a metric
space is often analyzed by computing the persistence diagram or barcodes of a
sequence of Rips complexes built on $P$ indexed by a scale parameter.
Unfortunately, even for input of moderate size, the size of the Rips complex
may become prohibitively large as the scale parameter increases. Starting with
the Sparse Rips filtration introduced by Sheehy, some existing methods aim to
reduce the size of the complex so as to improve the time efficiency as well.
However, as we demonstrate, existing approaches still fall short of scaling
well, especially for high dimensional data. In this paper, we investigate the
advantages and limitations of existing approaches. Based on insights gained
from the experiments, we propose an efficient new algorithm, called SimBa, for
approximating the persistent homology of Rips filtrations with quality
guarantees. Our new algorithm leverages a batch collapse strategy as well as a
new sparse Rips-like filtration. We experiment on a variety of low and high
dimensional data sets. We show that our strategy presents a significant size
reduction, and our algorithm for approximating Rips filtration persistence is
order of magnitude faster than existing methods in practice.
