Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Adaptive dimension reduction for clustering high dimensional data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Document clustering via adaptive subspace iteration
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Discriminative cluster analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
Nonlinear adaptive distance metric learning for clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
In multimedia applications, dimension reduction is essential to the effectiveness and efficiency of an algorithm due to the curse of dimensionality. Recently, its adaptive variants have received considerable attention in unsupervised learning since a single pass without label information often fails to guarantee an optimal representation, especially when the parameters are not set properly. However, most such methods are basically linear, therefore unable to consider the geometrical structure of the data space. In this paper, we propose a novel algorithm called Nonlinear Adaptive Dimension Reduction (NADR), which adaptively learns the optimal low-dimensional coordinates that preserve the intrinsic geometric structure of the original data. Moreover, the incorporation of K-means enables NADR to be a powerful alternative for cluster analysis. Experiments on benchmark image data sets illustrate that NADR outperforms the state-of-the-art adaptive dimension reduction methods.