Self-Organizing Maps
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Journal of Cognitive Neuroscience
The Journal of Machine Learning Research
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Statistical analysis of network traffic for adaptive faults detection
IEEE Transactions on Neural Networks
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Many types of data we are facing today are non-vectorial. But most of the analysis techniques are based on vector spaces and heavily depend on the underlying vector space properties. In order to apply such vector space techniques to non-vectorial data, so far only highly specialized methods have been suggested. We present a uniform and general approach to construct vector spaces from non-vectorial data. For this we develop a procedure to map each data element in a special kind of coordinate space which we call redundant dictionary space (RDS). The mapped vector space elements can be added, scaled and analyzed like vectors and thus allows any vector space analysis techniques to be used with any kind of data. The only requirement is the existence of a suitable inner product kernel.