|
|
 |
| |
|
|
|
Semi-supervised Sparsity Pairwise Constraint Preserving Projections based on GA |
|
|
|
PP: 1065-1075 |
|
|
|
Author(s) |
|
|
|
Mingming Qi,
Yang Xiang,
|
|
|
|
Abstract |
|
|
| The deficiency of the ability for preserving global geometric structure information of data is the main problem of existing
semi-supervised dimensionality reduction with pairwise constraints. A dimensionality reduction algorithm called Semi-supervised
Sparsity Pairwise Constraint Preserving Projections based on Genetic Algorithm (SSPCPPGA) is proposed. On the one hand, the
algorithm fuses unsupervised sparse reconstruction feature information and supervised pairwise constraint feature information in the
process of dimensionality reduction, preserving geometric structure in samples and constraint relation of samples simultaneously.
On the other hand , the algorithm introduces the genetic algorithm to set automatically the weighted trade-off parameter for full
fusion. Experiments operated on real world datasets show, in contrast to the existing typical semi-supervised dimensionality reduction
algorithms with pairwise constraints and other semi-supervised dimensionality reduction algorithms on sparse representation, the
proposed algorithm is more efficient. |
|
|
|
|
|
|
|
