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ACM Computing Surveys (CSUR)
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Building k Edge-Disjoint Spanning Trees of Minimum Total Length for Isometric Data Embedding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified K-Means Algorithm for Circular Invariant Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Incremental locally linear embedding
Pattern Recognition
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Fusion of locally linear embedding and principal component analysis for face recognition (FLLEPCA)
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Self-organized locally linear embedding for nonlinear dimensionality reduction
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Selection of the optimal parameter value for the ISOMAP algorithm
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Local relative transformation with application to isometric embedding
Pattern Recognition Letters
Neighborhood selection and eigenvalues for embedding data complex in low dimension
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
About eigenvalues from embedding data complex in low dimension
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Improved locally linear embedding by cognitive geometry
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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This paper proposes a clustering-based nonlinear dimensionality reduction approach. It utilizes the clustering approaches to form the clustering structure by which the distance between any two data points are rescaled to make data points from different clusters separated more easily. This rescaled distance matrix is then provided to improve the nonlinear dimensionality reduction approaches such as Isomap to achieve the better performance. Furthermore, the proposed approach also decreases the time complexity on the large data sets, as it provides good neighborhood structure that can speed up the subsequent dimensionality reducing process. Unlike the supervised approaches, this approach does not take the labelled data set as prerequisite, so that it is unsupervised. This makes it applicable to the broader domains. The conducted experiments by classification on benchmark data sets have validated the proposed approach.