Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Clustering in large graphs and matrices
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
TalkMine: a soft computing approach to adaptive knowledge recommendation
Soft computing agents
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stability-based validation of clustering solutions
Neural Computation
Feature Discovery in Non-Metric Pairwise Data
The Journal of Machine Learning Research
On the perceptual organization of image databases using cognitive discriminative biplots
EURASIP Journal on Applied Signal Processing
Generative models for similarity-based classification
Pattern Recognition
Linear-Time Computation of Similarity Measures for Sequential Data
The Journal of Machine Learning Research
Dealing with non-metric dissimilarities in fuzzy central clustering algorithms
International Journal of Approximate Reasoning
Fast k most similar neighbor classifier for mixed data (tree k-MSN)
Pattern Recognition
On the relevance of linear discriminative features
Information Sciences: an International Journal
Topographic mapping of large dissimilarity data sets
Neural Computation
Relational generative topographic mapping
Neurocomputing
Determining the cause of negative dissimilarity eigenvalues
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
A new anticorrelation-based spectral clustering formulation
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Clustering very large dissimilarity data sets
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Graph transduction as a noncooperative game
Neural Computation
The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
White box classification of dissimilarity data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Data analysis of (non-)metric proximities at linear costs
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
| Hi-index | 0.01 |
Two common data representations are mostly used in intelligent data analysis, namely the vectorial and the pairwise representation. Pairwise data which satisfy the restrictive conditions of Euclidean spaces can be faithfully translated into a Euclidean vectorial representation by embedding. Non-metric pairwise data with violations of symmetry, reflexivity or triangle inequality pose a substantial conceptual problem for pattern recognition since the amount of predictive structural information beyond what can be measured by embeddings is unclear. We show by systematic modeling of non-Euclidean pairwise data that there exists metric violations which can carry valuable problem specific information. Furthermore, Euclidean and non-metric data can be unified on the level of structural information contained in the data. Stable component analysis selects linear subspaces which are particularly insensitive to data fluctuations. Experimental results from different domains support our pattern recognition strategy.