Large-Scale Clustering through Functional Embedding
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Geodesic Generative Topographic Mapping
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Fast correlation analysis on time series datasets
Proceedings of the 17th ACM conference on Information and knowledge management
Bregman Divergences and the Self Organising Map
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
LDR-LLE: LLE with Low-Dimensional Neighborhood Representation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data
Similarity-Based Clustering
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Similarity-Based Clustering
Learning Representation and Control in Markov Decision Processes: New Frontiers
Foundations and Trends® in Machine Learning
EURASIP Journal on Advances in Signal Processing
SPARCL: an effective and efficient algorithm for mining arbitrary shape-based clusters
Knowledge and Information Systems
On the Manifold Structure of the Space of Brain Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Simbed: Similarity-Based Embedding
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Application notes: data mining in cancer research
IEEE Computational Intelligence Magazine
Visualizing dissimilarity data using generative topographic mapping
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Multidimensional data visualization applied for user's questionnaire data quality assessment
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Application of SOM-based visualization maps for time-response analysis of industrial processes
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Extending metric multidimensional scaling with Bregman divergences
Pattern Recognition
Feature selection and occupancy classification using seismic sensors
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Extending metric multidimensional scaling with bregman divergences
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Relevance learning in generative topographic mapping
Neurocomputing
Domain transform for edge-aware image and video processing
ACM SIGGRAPH 2011 papers
Curvature analysis of frequency modulated manifolds in dimensionality reduction
Calcolo: a quarterly on numerical analysis and theory of computation
Divergence-based vector quantization
Neural Computation
Baby morse theory in data analysis
Proceedings of the 2011 workshop on Knowledge discovery, modeling and simulation
Prediction-oriented dimensionality reduction of industrial data sets
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Sparse functional relevance learning in generalized learning vector quantization
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
A general framework for dimensionality reduction for large data sets
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
A new framework for assets selection based on dimensions reduction techniques
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
A study of embedding methods under the evidence accumulation framework
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Comparative analysis of power consumption in university buildings using envSOM
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Where traffic meets DNA: mobility mining using biological sequence analysis revisited
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Cohort-based kernel visualisation with scatter matrices
Pattern Recognition
A general framework for dimensionality-reducing data visualization mapping
Neural Computation
Novelty detection in projected spaces for structural health monitoring
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
An algorithm for sample and data dimensionality reduction using fast simulated annealing
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Towards the reduction of data used for the classification of network flows
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A nonlinear method for dimensionality reduction of data using reference nodes
Pattern Recognition and Image Analysis
Data-based modeling and monitoring for multimode processes using local tangent space alignment
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Combining neural methods and knowledge-based methods in accident management
Advances in Artificial Neural Systems
Parsimonious Mahalanobis kernel for the classification of high dimensional data
Pattern Recognition
Sparse embedding: a framework for sparsity promoting dimensionality reduction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Learning relevant time points for time-series data in the life sciences
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
How to quantitatively compare data dissimilarities for unsupervised machine learning?
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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
Incremental self-organizing map (iSOM) in categorization of visual objects
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Visualizing the quality of dimensionality reduction
Neurocomputing
Topology preserving hashing for similarity search
Proceedings of the 21st ACM international conference on Multimedia
Visual analysis of a cold rolling process using a dimensionality reduction approach
Engineering Applications of Artificial Intelligence
Using nonlinear dimensionality reduction to visualize classifiers
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Computers in Biology and Medicine
Dimensionality reduction with adaptive graph
Frontiers of Computer Science: Selected Publications from Chinese Universities
Supervised Distance Preserving Projections
Neural Processing Letters
Improvement of surface roughness models for face milling operations through dimensionality reduction
Integrated Computer-Aided Engineering
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Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods. This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings. The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.