Notwithstanding the popularity of conventional clustering algorithms such as K-means and probabilistic clustering, their clustering results are sensitive to the presence of outliers in the data. Even a few outliers can compromise the ability of these algorithms to identify meaningful hidden structures rendering their outcome unreliable. This paper develops robust clustering algorithms that not only aim to cluster the data, but also to identify the outliers. The novel approaches rely on the infrequent presence of outliers in the data, which translates to sparsity in a judiciously chosen domain. Leveraging sparsity in the outlier domain, outlier-aware robust K-means and probabilistic clustering approaches are proposed. Their novelty lies on identifying outliers while effecting sparsity in the outlier domain through carefully chosen regularization. A block coordinate descent approach is developed to obtain iterative algorithms with convergence guarantees and small excess computational complexity with respect to their non-robust counterparts. Kernelized versions of the robust clustering algorithms are also developed to efficiently handle high-dimensional data, identify nonlinearly separable clusters, or even cluster objects that are not represented by vectors. Numerical tests on both synthetic and real datasets validate the performance and applicability of the novel algorithms.
|Original language||English (US)|
|Number of pages||15|
|Journal||IEEE Transactions on Signal Processing|
|State||Published - 2012|
Bibliographical noteFunding Information:
Manuscript received July 18, 2011; revised January 04, 2012; accepted April 05, 2012. Date of publication April 26, 2012; date of current version July 10, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Raviv Raich. The work in this paper was supported in part by the AFOSR MURI Grant FA9550-10-1-0567. The work of V. Kekatos was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme (No. 234914).
- (Block) coordinate descent
- expectation-maximization algorithm
- kernel methods
- mixture models