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دسته بندی:
مهندسی مکانیک - mechanical engineering
سال انتشار:
2016
عنوان انگلیسی مقاله:
One class proximal support vector machines
ترجمه فارسی عنوان مقاله:
یک ماشین بردار پشتیبانی از یک کلاس پروکسیمال
منبع:
Sciencedirect - Elsevier - Pattern Recognition, 52 + (2016) 96-112: doi:10:1016/j:patcog:2015:09:036
نویسنده:
F. Dufrenois, J.C. Noyer
چکیده انگلیسی:
Recently in Dufrenois [1], a new Fisher type contrast measure has been proposed to extract a target
population in a dataset contaminated by outliers. Although mathematically sound, this work presents
some further shortcomings in both the formalism and the field of use. First, we propose to re-express this
problem from the formalism of proximal support vector machines as introduced in Mangasarian and
Wild [2]. This change is far from harmless since it introduces a suited writing for solving the problem.
Another limiting factor of the method is that its performance relies on the assumption that the density
between the target and outliers are different. This consideration can easily prove to be over-optimistic for
real world datasets making the method unreliable, at least directly. The computation of the decision
boundary is a time consuming part of the algorithm since it is based on solving a generalized eigenvalue
problem (GEP). This method is therefore limited to medium sized data sets. In this paper, we propose
appropriate strategies to unlock all these shortcomings and fully benefit from the interest of the
approach. Firstly, we show under some conditions that generating appropriate artificial outliers allows to
stay within the constraints of the method and thus enlarges the conditions of use. Secondly, we show
that the GEP can be advantageously replaced by a conjugate gradient solution (CG) significantly
decreasing the computational cost. Lastly, the proposed algorithm is compared with recent novelty
detectors on synthetic and real datasets.
Keywords:Outlier detection|Proximal support vector machines|Linear programming problem|Contrast measure
قیمت: رایگان
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