Back to Search
Start Over
Adaptive Local Embedding Learning for Semi-Supervised Dimensionality Reduction.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Oct2022, Vol. 34 Issue 10, p4609-4621, 13p
- Publication Year :
- 2022
-
Abstract
- Semi-supervised learning as one of most attractive problems in machine learning research field has aroused broad attentions in recent years. In this paper, we propose a novel locality preserved dimensionality reduction framework, named Semi-supervised Adaptive Local Embedding learning (SALE), which learns a local discriminative embedding by constructing a $k_1$ k 1 Nearest Neighbors ($k_1$ k 1 NN) graph on labeled data, so as to explore the intrinsic structure, i.e., sub-manifolds from non-Gaussian labeled data. Then, mapping all samples into learned embedding and constructing another $k_2$ k 2 NN graph on all embedded data to explore the global structure of all samples. Therefore, the unlabeled data and their corresponding labeled neighbors can be clustered into same sub-manifold, so as to improve the discriminative power of embedded data. Furthermore, we propose two semi-supervised dimensionality reduction methods with orthogonal and whitening constraints based on proposed SALE framework. An efficient alternatively iterative optimization algorithm is developed to solve the NP-hard problem in our models. Extensive experiments conducted on several synthetic and real-world data sets demonstrate the superiorities of our methods on local structure exploration and classification task. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 34
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 159210918
- Full Text :
- https://doi.org/10.1109/TKDE.2021.3049371