Abstract
In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure. In this paper, we consider a class of binary matrices, arising in many applications, which exhibit both row and column cluster structure, and our goal is to exactly recover the underlying row and column clusters by observing only a small fraction of noisy entries. We first derive a lower bound on the minimum number of observations needed for exact cluster recovery. Then, we study three algorithms with different running time and compare the number of observations needed by them for successful cluster recovery. Our analytical results show smooth time-data trade-offs: one can gradually reduce the computational complexity when increasingly more observations are available.
Original language | English (US) |
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Pages (from-to) | 29-41 |
Number of pages | 13 |
Journal | Performance Evaluation Review |
Volume | 42 |
Issue number | 1 |
DOIs | |
State | Published - Jun 20 2014 |
Event | ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2014 - Austin, United States Duration: Jun 16 2014 → Jun 20 2014 |
Keywords
- Clustering
- Low-rank matrix recovery
- Spectral method
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Networks and Communications