© Springer Science+Business Media New York 2013. In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant.
|Title of host publication||Graph Embedding for Pattern Analysis|
|Place of Publication||Dordrecht (NL)|
|Number of pages||26|
|Publication status||Published - 1 Jan 2013|