A Shrinkage Approach for Modeling Non-Stationary Relational Autocorrelation

Download

index.pdf

Date

2008-12-19

Author

Angın, Pelin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

164
views

0
downloads

Recent research has shown that collective classification in relational data often exhibit significant performance gains over conventional approaches that classify instances individually. This is primarily due to the presence of autocorrelation in relational datasets, meaning that the class labels of related entities are correlated and inferences about one instance can be used to improve inferences about linked instances. Statistical relational learning techniques exploit relational autocorrelation by modeling global autocorrelation dependencies under the assumption that the level of autocorrelation is stationary throughout the dataset. To date, there has been no work examining the appropriateness of this stationarity assumption. In this paper; we examine two real-world datasets and show that there is significant variance in the autocorrelation dependencies throughout the relational data graphs. We develop a shrinkage technique for modeling this non-stationary autocorrelation and show that it achieves significant accuracy gains over competing techniques that model either local or global autocorrelation dependencies in isolation.

Subject Keywords

Networks

URI

https://hdl.handle.net/11511/42213

DOI

https://doi.org/10.1109/icdm.2008.147

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Comparison of two inference approaches in Gaussian graphical models Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01) Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.
A novel neural network based approach for direction of arrival estimation Çaylar, Selçuk; Dural Ünver, Mevlüde Gülbin; Department of Electrical and Electronics Engineering (2007) In this study, a neural network(NN) based algorithm is proposed for real time multiple source tracking problem based on a previously reported work. The proposed algorithm namely modified neural network based multiple source tracking algorithm (MN-MUST) performs direction of arrival(DoA) estimation in three stages which are the detection, filtering and DoA estimation stages. The main contributions of this proposed system are: reducing the input size for the uncorrelated source case (reducing the training tim...
LIFE SITUATIONS AND ATTITUDES OF THE TURKISH ELDERLY TOWARD INSTITUTIONAL LIVING WITHIN A CROSS-CULTURAL-PERSPECTIVE IMAMOGLU, EO; İmamoğlu, Vacit (1992-03-01) Based on the findings of a cross-cultural study, this paper aims (a) to explore changes in the current life situations and attitudes of the Turkish elderly toward institutional living in terms of age, sex, and urbanization; and (b) by way of comparison with the Swedish case, to assess the generalizability of the findings within a cross-cultural perspective. Indepth interviews were carried out with 448 Turkish males and females (55-71 years) residing in small towns, cities, and metropolitan areas. The comp...
Robust background normalization method for one-channel microarrays AKAL, TÜLAY; Purutçuoğlu Gazi, Vilda; Weber, Gerhard-Wilhelm (Walter de Gruyter GmbH, 2017-04-01) Background: Microarray technology, aims to measure the amount of changes in transcripted messages for each gene by RNA via quantifying the colour intensity on the arrays. But due to the different experimental conditions, these measurements can include both systematic and random erroneous signals. For this reason, we present a novel gene expression index, called multi-RGX (Multiple-probe Robust Gene Expression Index) for one-channel microarrays.
MODELLING LIPID BIOSYNTHESIS PATHWAYS OF OIL PALM BY BOOLEAN AND GRAPHICAL APPROACHES Quek, Emily Ming Poh; Purutçuoğlu Gazi, Vilda; Sambanthamurthi, Ravigadevi; WilhelmWeber, Gerhard (2011-05-05) The mathematical modelling provides an alternative way for the interpretation and prediction of cellular metabolisms. This study delves into the modelling of lipid biosynthesis pathway and the primary metabolism in the fruit of the oil palm (Elaeis guineensis). Two different data are used for the analysis. In the first dataset, the distribution of carbon flux into the major classes of lipids, i.e., triacylglycerols, diacylglycerols, monoacylglycerols, phospholipids, and free fatty acids, is investigated usi...

Citation Formats

P. Angın, “A Shrinkage Approach for Modeling Non-Stationary Relational Autocorrelation,” 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42213.