Person-independent head pose estimation using biased manifold embedding
EURASIP Journal on Advances in Signal Processing
Feature extraction using constrained maximum variance mapping
Pattern Recognition
Weighted Kernel Isomap for Data Visualization and Pattern Classification
Computational Intelligence and Security
A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
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
Text Classification on Embedded Manifolds
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Dimensionality reduction for heterogeneous dataset in rushes editing
Pattern Recognition
Local relative transformation with application to isometric embedding
Pattern Recognition Letters
Supervised projection approach for boosting classifiers
Pattern Recognition
Enhanced supervised locally linear embedding
Pattern Recognition Letters
Using graph algebra to optimize neighborhood for isometric mapping
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning a locality discriminating projection for classification
Knowledge-Based Systems
Dimensionality reduction oriented toward the feature visualization for ischemia detection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
A volume-based heat-diffusion classifier
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Manifold-based learning and synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
Clustering-based nonlinear dimensionality reduction on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Performing locally linear embedding with adaptable neighborhood size on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Functional near infrared spectroscopy in novice and expert surgeons: a manifold embedding approach
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Learning manifolds for bankruptcy analysis
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Linear discriminant projection embedding based on patches alignment
Image and Vision Computing
Expert Systems with Applications: An International Journal
Dimensionality reduction techniques for blog visualization
Expert Systems with Applications: An International Journal
Robust head pose estimation using supervised manifold learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Orthogonal local spline discriminant projection with application to face recognition
Pattern Recognition Letters
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
The Journal of Machine Learning Research
Relevance learning in generative topographic mapping
Neurocomputing
Nonlinear dimensionality reduction using a temporal coherence principle
Information Sciences: an International Journal
A novel multi-view learning developed from single-view patterns
Pattern Recognition
A visualization metric for dimensionality reduction
Expert Systems with Applications: An International Journal
Immunodomaince based Clonal Selection Clustering Algorithm
Applied Soft Computing
An effective double-bounded tree-connected Isomap algorithm for microarray data classification
Pattern Recognition Letters
Neighbor line-based locally linear embedding
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A supervised non-linear dimensionality reduction approach for manifold learning
Pattern Recognition
Ontology-Based similarity between text documents on manifold
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
A general framework for dimensionality-reducing data visualization mapping
Neural Computation
Supervised subspace projections for constructing ensembles of classifiers
Information Sciences: an International Journal
Kernel ridge regression for out-of-sample mapping in supervised manifold learning
Expert Systems with Applications: An International Journal
Gene transposon based clone selection algorithm for automatic clustering
Information Sciences: an International Journal
Credit scoring for SME using a manifold supervised learning algorithm
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A regularization framework in polar coordinates for transductive learning in networked data
Information Sciences: an International Journal
Stability of dimensionality reduction methods applied on artificial hyperspectral images
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Discriminative dimensionality reduction mappings
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Out-of-sample embedding by sparse representation
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Automatic dimensionality estimation for manifold learning through optimal feature selection
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A comparative study of nonlinear manifold learning methods for cancer microarray data classification
Expert Systems with Applications: An International Journal
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
A supervised orthogonal discriminant projection for tumor classification using gene expression data
Computers in Biology and Medicine
Scalable supervised dimensionality reduction using clustering
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Embedding new observations via sparse-coding for non-linear manifold learning
Pattern Recognition
Discriminative functional analysis of human movements
Pattern Recognition Letters
Regularized discriminant entropy analysis
Pattern Recognition
Isometric sliced inverse regression for nonlinear manifold learning
Statistics and Computing
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
Kernel clustering using a hybrid memetic algorithm
Natural Computing: an international journal
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When performing visualization and classification, people often confront the problem of dimensionality reduction. Isomap is one of the most promising nonlinear dimensionality reduction techniques. However, when Isomap is applied to real-world data, it shows some limitations, such as being sensitive to noise. In this paper, an improved version of Isomap, namely S-Isomap, is proposed. S-Isomap utilizes class information to guide the procedure of nonlinear dimensionality reduction. Such a kind of procedure is called supervised nonlinear dimensionality reduction. In S-Isomap, the neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points, which is specially designed to integrate the class information. The dissimilarity has several good properties which help to discover the true neighborhood of the data and, thus, makes S-Isomap a robust technique for both visualization and classification, especially for real-world problems. In the visualization experiments, S-Isomap is compared with Isomap, LLE, and WeightedIso. The results show that S-Isomap performs the best. In the classification experiments, S-Isomap is used as a preprocess of classification and compared with Isomap, WeightedIso, as well as some other well-established classification methods, including the K-nearest neighbor classifier, BP neural network, J4.8 decision tree, and SVM. The results reveal that S-Isomap excels compared to Isomap and WeightedIso in classification, and it is highly competitive with those well-known classification methods.