Analysis on the Applicability of Meter Data Acquisition Methods Based on the Analytic Hierarchy Process

The choice of meter data acquisition methods has important significance for the electric energy management. Based on the comprehensive analysis of several meter data acquisition methods, this paper assess the performance of each one by analytic hierarchy process. We can draw a conclusion by calculating＂ The local automatic meter reading, the prepaid electric energy metering and the remote automatic meter reading have almost the same performance. They are better than the manual meter reading and the vehicle mounted mobile automatic meter reading. So we can choose any one of the three. Among them, the prepaid electric energy metering performs best. This can be a reference for grid company＇ s decision.

Towards kernelizing the classifier for hyperbolic data

Data hierarchy,as a hidden property of data structure,exists in a wide range of machine learning applications.A common practice to classify such hierarchical data is first to encode the data in the Euclidean space,and then train a Euclidean classifier.However,such a paradigm leads to a performance drop due to distortion of data embedding in the Euclidean space.To relieve this issue,hyperbolic geometry is investigated as an alternative space to encode the hierarchical data for its higher ability to capture the hierarchical structures.Those methods cannot explore the full potential of the hyperbolic geometry,in the sense that such methods define the hyperbolic operations in the tangent plane,causing the distortion of data embeddings.In this paper,we develop two novel kernel formulations in the hyperbolic space,with one being positive definite(PD)and another one being indefinite,to solve the classification tasks in hyperbolic space.The PD one is defined via mapping the hyperbolic data to the Drury-Arveson(DA)space,which is a special reproducing kernel Hilbert space(RKHS).To further increase the discrimination of the classifier,an indefinite kernel is further defined in the Krein spaces.Specifically,we design a 2-layer nested indefinite kernel which first maps hyperbolic data into the DA spaces,followed by a mapping from the DA spaces to the Krein spaces.Extensive experiments on real-world datasets demonstrate the superiority ofthe proposed kernels.