Name
Abstract | ||
|---|---|---|
Subspace clustering has typically been approached as an unsupervised machine learning problem. However in several applications where the union of subspaces model is useful, it is also reasonable to assume you have access to a small number of labels. In this paper we investigate the benefit labeled data brings to the subspace clustering problem. We focus on incorporating labels into the k-subspaces algorithm, a simple and computationally efficient alternating estimation algorithm. We find that even a very small number of randomly selected labels can greatly improve accuracy over the unsupervised approach. We demonstrate that with enough labels, we get a significant improvement by using actively selected labels chosen for points that are nearly equidistant to more than one estimated subspace. We show this improvement on simulated data and face images. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CAMSAP.2015.7383815 | 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
Keywords | Field | DocType |
margin-based active subspace clustering,unsupervised machine learning problem,k-subspaces algorithm,computationally efficient alternating estimation algorithm,unsupervised approach,actively selected labels,simulated data,face images | Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Constrained clustering,Cluster analysis | Conference |
Citations | PageRank | References |
2 | 0.36 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Lipor | 1 | 18 | 3.53 |
| Laura Balzano | 2 | 410 | 27.51 |