Publications

The performance of k-means clustering is often degenerate when dealing with high-dimensional and noisy scenarios. In this study, an end-to-end robust clustering method with low-rank linear embedding techniques (RCLR) is presented in conjunction with k-means. Sparse coefficients and a space projection matrix can be simultaneously learned. The global structures and local neighborhood properties are well captured in the learning procedures. Both the processes of clustering and dimensionality reduction are realized at the same time. The notions of clustering, dimensionality reduction, low-rank representation, and local property preservation are seamlessly integrated into a unified model. The limitation of error accumulation encountered in the previous two-stage clustering framework involving low-rank representation can be alleviated. This is the first attempt to introduce both the global and local geometrical structures into k-means directly, as well L2,1-norm is used as a basic metric instead of the conventional F-norm to further improve the robustness and interpretation of the model. The superiority of the proposed RCLR method is demonstrated by extensive experiments completed on various well-known benchmark datasets.

Article Free Access Share on Information granulation for concept formation Authors: W. Pedrycz Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, T6G 2G7 Canada and Institute of Medical Technology and Equipment (ITAM), 118 Roosevelt st., Zabrze 41-800, Poland Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, T6G 2G7 Canada and Institute of Medical Technology and Equipment (ITAM), 118 Roosevelt st., Zabrze 41-800, PolandView Profile , A. V. Vasilakos Foundation for Research and Technology - Hellas - FORTH, Institute of Computer Science - ICS, Telecommunications & Networks Group, PO Box 13 85, 71110 Heraklion, Greece Foundation for Research and Technology - Hellas - FORTH, Institute of Computer Science - ICS, Telecommunications & Networks Group, PO Box 13 85, 71110 Heraklion, GreeceView Profile , A. Gacek Institute of Medical Technology and Equipment (ITAM), 118 Roosevelt st., Zabrze 41-800, Poland Institute of Medical Technology and Equipment (ITAM), 118 Roosevelt st., Zabrze 41-800, PolandView Profile Authors Info & Claims SAC '00: Proceedings of the 2000 ACM symposium on Applied computing - Volume 1March 2000Pages 484–489https://doi.org/10.1145/335603.335922Published:19 March 2000Publication History 3citation413DownloadsMetricsTotal Citations3Total Downloads413Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

While many techniques exist to classify data possessing straightforward characteristics, they tend to fail when dealing with the ldquocurse of dimensionalityrdquo. This condition, in which the ratio of features to samples is very large, is prevalent in many complex, voluminous biomedical datasets acquired using current spectroscopic modalities. We present a novel classification method using an adaptive network of fuzzy logic connectives to combine class boundaries generated by sets of linear discriminant functions. We empirically demonstrate the effectiveness of this method using a benchmark linear discriminant analysis approach with feature averaging.