Parallel coordinates for visualizing multi-dimensional geometry
CG International '87 on Computer graphics 1987
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Similarity metric learning for a variable-kernel classifier
Neural Computation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Animating multidimensional scaling to visualize N-dimensional data sets
INFOVIS '96 Proceedings of the 1996 IEEE Symposium on Information Visualization (INFOVIS '96)
Dimensionality reduction using magnitude and shape approximations
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Distance-Preserving Projection of High-Dimensional Data for Nonlinear Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Auto-associative models and generalized principal component analysis
Journal of Multivariate Analysis
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
High dimensional nearest neighbor searching
Information Systems
Manifold reconstruction in arbitrary dimensions using witness complexes
SCG '07 Proceedings of the twenty-third annual symposium on Computational geometry
Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps
Proceedings of the 24th international conference on Machine learning
Person-independent head pose estimation using biased manifold embedding
EURASIP Journal on Advances in Signal Processing
Supervised Isomap with Dissimilarity Measures in Embedding Learning
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Dimensionality reduction for heterogeneous dataset in rushes editing
Pattern Recognition
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Similarity-Based Clustering
Enhanced supervised locally linear embedding
Pattern Recognition Letters
Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
Using graph algebra to optimize neighborhood for isometric mapping
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dimensionality reduction oriented toward the feature visualization for ischemia detection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
A template-based isomap algorithm for real-time removal of ocular artifacts from EEG signals
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Nonlinear embedding preserving multiple local-linearities
Pattern Recognition
Distinguishing variance embedding
Image and Vision Computing
Clustering-based nonlinear dimensionality reduction on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Efficient locally linear embeddings of imperfect manifolds
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Learning manifolds for bankruptcy analysis
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Supervised semi-definite embedding for email data cleaning and visualization
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
ISOLLE: locally linear embedding with geodesic distance
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Robust attentive behavior detection by non-linear head pose embedding and estimation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Evolving insight into high-dimensional data
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - 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
Improvement of data visualization based on ISOMAP
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Kernel ridge regression for out-of-sample mapping in supervised manifold learning
Expert Systems with Applications: An International Journal
Isometric sliced inverse regression for nonlinear manifold learning
Statistics and Computing
Hi-index | 0.00 |
In this paper we address the issue of using local embeddings for data visualization in two and three dimensions, and for classification. We advocate their use on the basis that they provide an efficient mapping procedure from the original dimension of the data, to a lower intrinsic dimension. We depict how they can accurately capture the user's perception of similarity in high-dimensional data for visualization purposes. Moreover, we exploit the low-dimensional mapping provided by these embeddings, to develop new classification techniques, and we show experimentally that the classification accuracy is comparable (albeit using fewer dimensions) to a number of other classification procedures.