Pointed Subspace Approach to Incomplete Data

Pointed subspace approach to incomplete data

Incomplete data are often represented as vectors with filled missing attributes joined with flag vectors indicating missing components. In this paper we generalize this approach and represent incomplete data as pointed affine subspaces. This allows to perform various affine transformations of data, as whitening or dimensionality reduction. We embed such generalized missing data into a vector sp...

a new approach to credibility premium for zero-inflated poisson models for panel data

هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...

Rough set approach to incomplete numerical data

The theory rough set successfully implemented diberbagai sector , but rough set model classical can only associated with the data complete and set of data in symbolic form ( Jianhua , dai , 2013 ) . Research by adopting the theory rough set conducted in attribute numerical and the value of an attribute lost ( Jerzy w , Grzymala-Buse and Zdzislaw S , Hippe , Nov .2011 ) .In this research discuss...

Image Reconstruction from Incomplete Data

Unified subspace learning for incomplete and unlabeled multi-view data

Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In ...