A Novel Subspace Clustering Method for Dictionary Design

Authors:
B. Vikrham Gowreesunker;Ahmed H. Tewfik
Affiliations:
Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455;Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455
Venue:
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Year:
2009

Citing 2
Cited 1

Two improved sparse decomposition methods for blind source separation

ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

IEEE Transactions on Signal Processing

Sparse coding with adaptive dictionary learning for underdetermined blind speech separation

Speech Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we propose a novel subspace clustering method for learning dictionaries from data. In our method, we seek to identify the subspaces where our data lives and find an orthogonal set of vectors that spans each of those subspaces. We use an Orthogonal Subspace Pursuit (OSP) decomposition method to identify the subspaces where the observation data lives, followed by a clustering operation to identify all observations that lives in the same subspace. This work is motivated by the need for faster dictionary training methods in blind source separation (BSS) of audio sources. We show that our dictionary design method offer considerable computational savings when compared to the K-SVD[1] for similar performance. Furthermore, the training method also offers better generalizability when evaluated on data beyond the training set, and consequently is well suited for continuously changing audio scenes.