Published in 2019 IEEE International Conference on Big Data, 2019
Subspace clustering is a growing field of unsupervised learning that has gained much popularity in the computer vision community. Applications can be found in areas such as motion segmentation and face clustering. It assumes that data originate from a union of subspaces, and clusters the data depending on their corresponding subspaces. In practice, it is reasonable to assume that a limited amount of labels can be obtained, potentially at a cost. Therefore, algorithms that can effectively and efficiently incorporate this information to improve the clustering model are desirable. Read more
Recommended citation: H. Peng, and N. Pavlidis, "Subspace Clustering with Active Learning", Proceedings of 2019 IEEE International Conference on Big Data, 2019.