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Showing 1 to 6 of 6 for “"Robust clustering"”.
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Harnessing Sparse and Low-Dimensional Structures for Robust Clustering of Imagery Data
We propose a robust framework for clustering data. In practice, data obtained from real measurement devices can be incomplete, corrupted by gross errors, or not correspond to any assumed model. We show that, by properly harnessing the intrinsic low-dimensional structure of the data, these kinds of …
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Robust methods for analyzing multivariate responses with application to time-course data
… We use Huber's loss function in developing robust methods for time-course multivariate responses. We use spline basis expansion of the time-varying regression coefficients to reduce dimensionality, and downweight the influence of outliers with Huber's loss function on vectors of residuals. …
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Multi-Center Federated Learning to Cluster Clients with non-IID data
… to solve non-IID challenges using a client clustering method in the FL context. However, even adopts a client clustering FL method still facing minor problems such as unstable against client-wise outliers and the drop of model performance with model poisoning attack. To face the …
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Contributions to Robust Methods: Modified Rank Covariance Matrix and Spatial-EM Algorithm
… to replace the sample covariance matrix with a robust one. Visuri et al. (2000) proposed a technique for robust covariance matrix estimation based on different notions of multivariate sign and rank. Among them, the spatial rank based covariance matrix estimator that utilizes a robust scale …
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Clustering and dimensionality reduction for time-series service monitoring data
… and non-linear. As the dataset is new, based on robust clustering approaches, we thoroughly assess the quality of the initial dataset and the reconstructed datasets (same dimensionality as the initial dataset) produced with Deep and Convolutional AutoEncoders. The experiments disclose that the …
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Robust techniques and applications in fuzzy clustering
… and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The …