Pattern Recognition with Vector Symbolic Architectures
Abstract: Pattern recognition is an area constantly enlarging its theoretical and practical horizons. Applications of pattern recognition and machine learning can be found in many areas of the present day world including health-care, robotics, manufacturing, economics, automation, transportation, etc. Despite some success in many domains pattern recognition algorithms are still far from being close to their biological vis-a-vis – human brain. New possibilities in the area of pattern recognition may be achieved by application of biologically inspired approaches. This thesis presents the usage of a bio-inspired method of representing concepts and their meaning – Vector Symbolic Architectures – in the context of pattern recognition with possible applications in intelligent transportation systems, automation systems, and language processing. Vector Symbolic Architectures is an approach for encoding and manipulating distributed representations of information. They have previously been used mainly in the area of cognitive computing for representing and reasoning upon semantically bound information. First, it is shown that Vector Symbolic Architectures are capable of pattern classification of temporal patterns. With this approach, it is possible to represent, learn and subsequently classify vehicles using measurements from vibration sensors.Next, an architecture called Holographic Graph Neuron for one-shot learning of patterns of generic sensor stimuli is proposed. The architecture is based on implementing the Hierarchical Graph Neuron approach using Vector Symbolic Architectures. Holographic Graph Neuron shows the previously reported performance characteristics of Hierarchical Graph Neuron while maintaining the simplicity of its design. The Holographic Graph Neuron architecture is applied in two domains: fault detection and longest common substrings search. In the area of fault detection the architecture showed superior performance compared to classical methods of artificial intelligence while featuring zero configuration and simple operations. The application of the architecture for longest common substrings search showed its ability to robustly solve the task given that the length of a common substring is longer than 4% of the longest pattern. Furthermore, the required number of operations on binary vectors is equal to the suffix trees approach, which is the fastest traditional algorithm for this problem. In summary, the work presented in this thesis extends understanding of the performance proprieties of distributed representations and opens the way for new applications.
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