Name
Abstract | ||
|---|---|---|
Many scientific datasets are of high dimension, and the analysis usually requires visual manipulation by retaining the most important structures of data. Principal curve is a widely used approach for this purpose. However, many existing methods work only for data with structures that are not self-intersected, which is quite restrictive for real applications. To address this issue, we develop a new model, which captures the local information of the underlying graph structure based on reversed graph embedding. A generalization bound is derived that show that the model is consistent if the number of data points is sufficiently large. As a special case, a principal tree model is proposed and a new algorithm is developed that learns a tree structure automatically from data. The new algorithm is simple and parameter-free with guaranteed convergence. Experimental results on synthetic and breast cancer datasets show that the proposed method compares favorably with baselines and can discover a breast cancer progression path with multiple branches. |
Year | Venue | Field |
|---|---|---|
2015 | SDM | Tree traversal,Graph embedding,Prim's algorithm,Computer science,K-ary tree,Algorithm,Tree structure,ID3 algorithm,Segment tree,Interval tree |
DocType | Citations | PageRank |
Conference | 3 | 0.39 |
References | Authors | |
9 | 5 | |