1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
Subspace learning theory for dimensionality reduction was initiated with the Principal Component Analysis (PCA) formulation proposed by Pearson in 1901. PCA was first widely used for data analysis in the field of psychometrics and chemometrics but today it is often the first step in more various types of exploratory data analysis, predictive modeling, classification and clustering problems. It finds modern applications in signal processing, biomedical imaging, computer vision, process fault detection, recommendation system design and many more domains. Since one century, numerous other subspace learning models, either reconstructive and discriminative, were developed over time in literature to address dimensionality reduction while keeping the relevant information in a different manner from PCA. However, PCA can also be viewed as a soft clustering method that seeks to find clusters in different subspaces within a dataset, and numerous clustering methods are based on dimensionality reduction. These methods are called subspace clustering methods that are extension of traditional PCA based clustering, and divide data points belonging to the union of subspaces (UoS) into the respective subspaces. In several modern applications, the main limitation of the subspace learning and clustering models are their sensitivity to outliers. Thus, further developments concern robust subspace learning which refers to the problem of subspace learning in the presence of outliers. In fact, even the classical subspace learning problem with speed or memory constraints is not a solved problem. These issues have become practically important for modern datasets because of the following reasons:
Thus, the special issue on Robust Subspace Learning and Tracking published in IEEE Journal of Selected Topics in Signal Processing in December 2018 group recent works in robust subspace learning and clustering related to theory, algorithms and applications for signal processing and computer vision applications. Several papers concern algorithms to address robustness of subspace learning against different kinds of outliers.
|Call for Nominations for Editor-in-Chief: IEEE Open Journal of Signal Processing||1 August 2019|
|Nominations Open for 2019 SPS Awards||1 September 2019|
|Call for Nominations: Technical Committee Vice Chair and Member Positions||15 September 2019|
|Call for Nominations: Awards Board and Nominations and Appointments Committee||27 September 2019|
|Election of President-Elect, Regional Directors-at-Large and Members-at-Large||1 October 2019|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2019|
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.