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In this paper we present a detailed theoretical analysis of a novel feature-based signature algorithm based on Bayesian optimization. We provide a first detailed analysis for Bayesian optimization of signature algorithms using the concept of Bayesian optimization problem which means that the objective function for a Bayesian implementation could be set by the sum of the probabilities of the parameters. In a Bayesian optimization problem there is a simple nonlinear objective function which, in general, can be modeled as a sub-problem. This analysis suggests that the method is capable of handling many types of anomaly and possibly to a large extent due to its nonlinearity (e.g., the number of parameters could be significantly larger than the number of samples). The new algorithm is named as Bayesian Optimized Signature (BISO) and is a fast and simple algorithm for signature algorithms (i.e., using the principle of maximum likelihood). Although BISO can be performed efficiently we also show that the algorithm can be solved in a scalable fashion and that the algorithm can be used to perform nonlinear optimization in many signature algorithms.

A Robust Binary Subspace Dictionary for Deep Unsupervised Domain Adaptation

Recurrent Convolutional Neural Network for Action Detection

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  • Diet in the Wild: Large-Scale Detection of Exercise-Related Events from Body States using Mobile Phones

    Unifying statistical and stylistic features in digital signature algorithmsIn this paper we present a detailed theoretical analysis of a novel feature-based signature algorithm based on Bayesian optimization. We provide a first detailed analysis for Bayesian optimization of signature algorithms using the concept of Bayesian optimization problem which means that the objective function for a Bayesian implementation could be set by the sum of the probabilities of the parameters. In a Bayesian optimization problem there is a simple nonlinear objective function which, in general, can be modeled as a sub-problem. This analysis suggests that the method is capable of handling many types of anomaly and possibly to a large extent due to its nonlinearity (e.g., the number of parameters could be significantly larger than the number of samples). The new algorithm is named as Bayesian Optimized Signature (BISO) and is a fast and simple algorithm for signature algorithms (i.e., using the principle of maximum likelihood). Although BISO can be performed efficiently we also show that the algorithm can be solved in a scalable fashion and that the algorithm can be used to perform nonlinear optimization in many signature algorithms.


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