Research map / Learning theory and algorithms / Clustering
K-means clustering using random matrix sparsification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| non-uniform random sampling instead of uniform sampling for matrix sparsification (ablation) | k-means on sparsified data matrix · USPS, TDT2, RCV1 | k-means clustering quality (NMI, objective ratio h1, h3) | Helped |
| non-uniform sampling vs uniform sampling at same nonzero fraction (ablation) | k-means on TDT2 and RCV1 · TDT2, RCV1 | k-means clustering performance | Helped |
| increase fraction of nonzero entries q (less sparsification) | k-means on sparsified data matrix · USPS, TDT2, RCV1 | objective ratio h1(q) and normalized objective h3(q) | Helped |
| random projection to 1000 dimensions before sparsification | TDT2 and RCV1 sparse text data · TDT2, RCV1 | normalized mutual information | No clear effect |
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