  Orthogonal Matching Pursuit (OMP) plays an important role in data science and
its applications such as sparse subspace clustering and image processing.
However, the existing OMP-based approaches lack of data adaptiveness so that
the data cannot be represented well enough and may lose the accuracy. This
paper proposes a novel approach to enhance the data-adaptive capability for
OMP-based sparse subspace clustering. In our method a parameter selection
process is developed to adjust the parameters based on the data distribution
for information representation. Our theoretical analysis indicates that the
parameter selection process can efficiently coordinate with any OMP-based
methods to improve the clustering performance. Also a new
Self-Expressive-Affinity (SEA) ratio metric is defined to measure the sparse
representation conversion efficiency for spectral clustering to obtain data
segmentations. Our experiments show that proposed approach can achieve better
performances compared with other OMP-based sparse subspace clustering
algorithms in terms of clustering accuracy, SEA ratio and representation
quality, also keep the time efficiency and anti-noise ability.
