Introducing the Huber mechanism for differentially private low-rank matrix completion

no code implementations • 16 Jun 2022 • R Adithya Gowtham, Gokularam M, Thulasi Tholeti, Sheetal Kalyani

We also propose using the Iteratively Re-Weighted Least Squares algorithm to complete low-rank matrices and study the performance of different noise mechanisms in both synthetic and real datasets.

Low-Rank Matrix Completion Privacy Preserving

Subspace clustering without knowing the number of clusters: A parameter free approach

no code implementations • 10 Sep 2019 • Vishnu Menon, Gokularam M, Sheetal Kalyani

In this work, a parameter free method for subspace clustering is proposed, where the data points are clustered on the basis of the difference in statistical distribution of the angles subtended by the data points within a subspace and those by points belonging to different subspaces.

Clustering