Name
Abstract | ||
|---|---|---|
Dimension reduction methods are often used to analyzing high dimensional data, linear dimension methods are commonly used due to their simple geometric interpretations and for effective computational cost. Dimension reduction plays an important role for feature selection. In this paper, we have given a detailed comparison of state-of-the-art linear dimension reduction methods like principal component analysis (PCA), random projections (RP), and locality preserving projections (LPP). We have determined which dimension reduction method performs better under the FastTag Image annotation framework. Experiments are conducted on three standard bench mark image datasets such as CorelSk, IAPRTC-12 and ESP game to compare the efficiency, effectiveness and also memory usage. A detailed comparison among the aforementioned dimension reduction method is given. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICACI.2015.7184729 | 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI) |
Keywords | Field | DocType |
linear dimensionality reduction methods,high dimensional data analysis,geometric interpretations,feature selection,principal component analysis,PCA,random projections,RP,locality preserving projections,LPP,FastTag image annotation framework,Corel5k,IAPRTC-12,ESP game | Kernel (linear algebra),Clustering high-dimensional data,Automatic image annotation,Dimensionality reduction,Feature selection,Pattern recognition,Principal component regression,Kernel principal component analysis,Artificial intelligence,Principal component analysis,Mathematics | Conference |
ISBN | Citations | PageRank |
978-1-4799-7257-9 | 0 | 0.34 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| shiqiang li | 1 | 0 | 0.34 |
| Hussain Dawood | 2 | 53 | 12.90 |
| Ping Guo | 3 | 601 | 85.05 |