Name
Title | ||
|---|---|---|
Instance-Wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters. |
Abstract | ||
|---|---|---|
We address an ensemble clustering problem, where reliable clusters are locally embedded in given multiple partitions. We propose a new nonnegative matrix factorization (NMF)-based method, in which locally reliable clusters are explicitly considered by using instance-wise weights over clusters. Our method factorizes the input cluster assignment matrix into two matrices H and W, which are optimized by iteratively 1) updating H and W while keeping the weight matrix constant and 2) updating the weight matrix while keeping H and W constant, alternatively. The weights in the second step were updated by solving a convex problem, which makes our algorithm significantly faster than existing NMF-based ensemble clustering methods. We empirically proved that our method outperformed a lot of cutting-edge ensemble clustering methods by using a variety of datasets. |
Year | Venue | Field |
|---|---|---|
2015 | IJCAI | Cluster (physics),Mathematical optimization,Matrix (mathematics),Computer science,Algorithm,Non-negative matrix factorization,Cluster analysis,Convex optimization |
DocType | Citations | PageRank |
Conference | 5 | 0.38 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| xiaodong zheng | 1 | 82 | 4.27 |
| Shanfeng Zhu | 2 | 429 | 35.04 |
| Junning Gao | 3 | 25 | 1.69 |
| Hiroshi Mamitsuka | 4 | 973 | 91.71 |