Three clustering optimization algorithms based on dictionary learning

This paper proposes l
₂
norm, l
₁
norm and l
_∞
norm of clustering optimization algorithms based on dictionary learning. By solving an optimization problem to assign each feature to a cluster and solving another optimization problem to re-calculating the vectors representing the clusters, each algorithm keeps iterating until it converges. Computer simulation experiments show that the three algorithms have good clustering results and the convergence is confirmed. The runtime of l
₂
norm clustering optimization algorithm is much faster than h norm and l
_∞
norm clustering optimization algorithms.