Feature hashing algorithms [14], [21] are well-known algorithmic techniques for handling large dimensionality of the datasets. These methods reduce the dimensionality of data points while preserving the pairwise distance, or similarity between them. However, to the best of our knowledge, these feature hashing algorithms are not adaptable to the dynamic insertion and deletion of features. In this work, we suggest algorithms using which the existing feature hashing algorithms can be made adapted to dynamic feature insertion and deletion. Our algorithms fit in the framework of both real-valued [21], and binary [14] feature hashing algorithms. We show a theoretical analysis of our algorithms, and complement it with experiments on real-world datasets. Our algorithms suggest comparable accuracy w.r.t. baselines while simultaneously offering a significant speed-up in the running time. Our proposal is easy to implement and can be adopted in practice. © 2021 IEEE.