Computer architecture: a quantitative approach
Computer architecture: a quantitative approach
Relational discriminant analysis
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Using low-spectral-resolution images to acquire simulated hyperspectral images
International Journal of Remote Sensing
The dissimilarity representation as a tool for three-way data classification: a 2D measure
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Classification of three-way data by the dissimilarity representation
Signal Processing
A study on the influence of shape in classifying small spectral data sets
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Taxonomy of classifiers based on dissimilarity features
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Class proximity measures - Dissimilarity-based classification and display of high-dimensional data
Journal of Biomedical Informatics
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For the sake of classification, spectra are traditionally represented by points in a high-dimensional feature space, spanned by spectral bands. An alternative approach is to represent spectra by dissimilarities to other spectra. This relational representation enables one to treat spectra as connected entities and to emphasize characteristics such as shape, which are difficult to handle in the traditional approach. Several classification methods for relational representations were developed and found to outperform the nearest-neighbor rule. Existing studies focus only on the performance measured by the classification error. However, for real-time spectral imaging applications, classification speed is of crucial importance. Therefore, in this paper, we focus on the computational aspects of the on-line classification of spectra. We show, that classifiers built in dissimilarity spaces may also be applied significantly faster than the nearest-neighbor rule.