Abstract
Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis.
Keywords
- Feature Vector
- Node Degree
- Numeric Attribute
- Node Attribute
- Statistical Pattern Recognition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Luqman, M.M., Ramel, JY., Lladós, J. (2013). Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces. In: Fu, Y., Ma, Y. (eds) Graph Embedding for Pattern Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4457-2_1
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