Name
Abstract | ||
|---|---|---|
An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Point Distribution algorithm which is initially developed for mining patterns between vector and item data. The subsequent clustering procedure is based on fitting a Gaussian mixture model on multiple random projection matrices. The final class label of each unit is determined by voting from the results of the random projection matrices. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICMLA.2010.161 | ICMLA |
Keywords | DocType | ISBN |
gaussian mixture model,speech processing,pattern clustering,random projection matrix,multiple random projection matrices,random processes,point distribution algorithm,final class label,matrix algebra,constructs affinity matrix,affinity matrices,sequential data,item data,sequential data clustering,mining patterns,gaussian processes,subsequent clustering procedure,vector data,speaker data,data mining,multiple random projection matrix,speaker clustering problem,random projection,kl-divergence,vectors,distributed algorithm,time series analysis,kernel,kl divergence,speech,data clustering,mathematical model,clustering algorithms,symmetric matrices | Conference | 978-1-4244-9211-4 |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianfei Wu | 1 | 40 | 2.77 |
| Loai Al Nimer | 2 | 1 | 0.35 |
| Omar Al Azzam | 3 | 5 | 2.57 |
| Charith Chitraranjan | 4 | 1 | 1.70 |
| Saeed Salem | 5 | 182 | 17.39 |
| Anne M. Denton | 6 | 94 | 10.96 |