SIAM Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
| Hi-index | 0.00 |
The Email systems are playing an important and irreplaceable role in the digital world due to its convenience, efficiency and the rapid growth of World Wide Web (WWW). However, most of the email users nowadays are suffering from the large amounts of irrelevant and noisy emails everyday. Thus algorithms which can clean both the noise features and the irrelevant emails are highly desired. In this paper, we propose a novel Supervised Semi-definite Embedding (SSDE) algorithm to reduce the dimension of email data so as to leave out the noise features of them and visualize these emails in a supervised manner to find the irrelevant ones intuitively. Experiments on a set of received emails of several volunteers during a period of time and some benchmark datasets show the comparable performance of the proposed SSDE algorithm.