Name
Abstract | ||
|---|---|---|
Classification tasks involving high dimensional vectors are affected by the curse of dimensionality requiring large amount of training data. This is because a high-dimensional space with a modest number of samples is mostly empty. To overcome this we employ the principle of Projection Pursuit. The principle is motivated by the aim to search for clusters in high-dimensional space. Data points are projected onto an appropriate projection direction. Search for clusters is in this single dimensional projection space. As a result inherent sparsity of the high-dimensional space is avoided. Classical discriminant analysis methods also seek clusters but require class labels to be specified. One such technique, the Fisher's linear discriminant (FLD) method, has been used to arrive at an unsupervised algorithm that seeks bimodal projection directions. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.247126 | IJCNN |
Keywords | Field | DocType |
pattern clustering,bimodal projection,pattern classification,fisher linear discriminant method,search problems,high-dimensional space,unsupervised algorithm,vectors,projective space,curse of dimensionality,discriminant analysis,projection pursuit | Training set,Data point,Cluster (physics),Pattern recognition,Projection pursuit,Pattern clustering,Computer science,Multiple discriminant analysis,Curse of dimensionality,Artificial intelligence,Linear discriminant analysis,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-4393 | 0-7803-9490-9 | 1 |
PageRank | References | Authors |
0.48 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dipti Deodhare | 1 | 18 | 5.14 |
| M. Vidyasagar | 2 | 122 | 32.78 |
| M. Narasimha Murty | 3 | 824 | 86.07 |