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. Capitalizing on the
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.Comment: Submitted to IEEE Trans. on PAM
The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. The E-step in the novel iterative scheme relies on local information available to individual sensors, while during the M-step sensors exchange information only with their onehop neighbors to reach consensus and eventually percolate the global information needed to estimate the wanted parameters across the wireless sensor network (WSN). Analysis and simulations demonstrate that the resultant consensus-based distributed EM (CB-DEM) algorithm matches well the resourcelimited characteristics of WSNs and compares favorably with existing alternatives because it has wider applicability and remains resilient to inter-sensor communication noise.
Using passive sonar for underwater acoustic source localization in a shallow-water environment is challenging due to the complexities of underwater acoustic propagation. Matched-field processing (MFP) exploits both measured and model-predicted acoustic pressures to localize acoustic sources. However, the ambiguity surface obtained through MFP contains artifacts that limit its ability to reveal the location of the acoustic sources. This work introduces a robust scheme for shallow-water source localization that exploits the inherent sparse structure of the localization problem and the use of a model characterizing the acoustic propagation environment. To this end, the underwater acoustic source-localization problem is cast as a sparsity-inducing stochastic optimization problem that is robust to model mismatch. The resulting source-location map (SLM) yields reduced ambiguities and improved resolution, even at low signal-to-noise ratios, when compared to those obtained via classical MFP approaches. An iterative solver based on block-coordinate descent is developed whose computational complexity per iteration is linear with respect to the number of locations considered for the SLM. Numerical tests illustrate the performance of the algorithm.
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