@article{2017-Kroll_Duerrbaum-ASOC-OED, abstract = {Optimal Experiment Design (OED) is a well-developed concept for regression problems that are linear-in-the-parameters. In case of experiment design to identify nonlinear Takagi-Sugeno (TS) models, non-model-based approaches or OED restricted to the local model parameters (assuming the partitioning to be given) have been proposed. In this article, a Fisher Information Matrix (FIM) based OED method is proposed that considers local model and partition parameters. Due to the nonlinear model, the FIM depends on the model parameters that are subject of the subsequent identification. To resolve this paradoxical situation, at first a model-free space filling design (such as Latin Hypercube Sampling) is carried out. The collected data permits making design decisions such as determining the number of local models and identifying the parameters of an initial TS model. This initial TS model permits a FIM-based OED, such that data is collected which is optimal for a TS model. The estimates of this first stage will in general not be ideal. To become robust against parameter mismatch, a sequential optimal design is applied. In this work the focus is on D-optimal designs. The proposed method is demonstrated for three nonlinear regression problems: an industrial axial compressor and two test functions.}, author = {Kroll, Andreas and Dürrbaum, Axel}, doi = {10.1016/j.asoc.2017.07.015}, interhash = {a2bd07c0fd0a6d9e64cc7d4ad05ece28}, intrahash = {53184f758e02356412cf7982b5977f90}, journal = {Applied Soft Computing}, language = {english}, mrtnote = {peer,OED}, mrturla = {http://141.51.48.24/MRT/Bibliothek/Publikationen/2017-Kroll_Duerrbaum-ASoC-OED-submitted-PUB.pdf}, owner = {duerrbaum}, pages = {407 -- 422}, title = {On optimal experiment design for identifying premise and conclusion parameters of Takagi-Sugeno models: nonlinear regression case}, url = {https://reader.elsevier.com/reader/sd/pii/S1568494617304246}, volume = 60, year = 2017 } @inproceedings{Duerrbaum-2015-SysID, abstract = {Optimal Experiment Design (OED) is a well-developed concept for regression problems that are linear-in-their-parameters or for linear dynamical models. In case of nonlinear Takagi-Sugeno models either non-model-based experiment design or OED restricted to the local model parameters has been examined. This article proposes a joint design of local model and partition parameters that bases on the Fisher Information Matrix (FIM). For this purpose, a symbolic description of the joint FIM is derived. Its heterogeneous structure can make it badly conditioned, complicating computation of the determinant for D-optimal design. This problem is relaxed using determinant decomposition. A theoretical analysis and a case study show that experiment design for local model and partition parameters may significantly differ from each other.}, address = {Beijing, China}, author = {Kroll, Andreas and Dürrbaum, Axel}, booktitle = {Proceedings of the 17th IFAC Symposium on System Identification ({SysID})}, doi = {doi:10.1016/j.ifacol.2015.12.333}, interhash = {c8b8e8d219667b0df23b145097939fd4}, intrahash = {a62f4bcd0dac434df35ebba90261698d}, language = {english}, month = {October 19-21}, mrtnote = {peer,talk:Dürrbaum,oed}, pages = {1427 -- 1432}, title = {On joint optimal experiment design for identifying partition and local model parameters of Takagi-Sugeno models}, year = 2015 } @inproceedings{Duerrbaum-2015-SysID, abstract = {Optimal Experiment Design (OED) is a well-developed concept for regression problems that are linear-in-their-parameters or for linear dynamical models. In case of nonlinear Takagi-Sugeno models either non-model-based experiment design or OED restricted to the local model parameters has been examined. This article proposes a joint design of local model and partition parameters that bases on the Fisher Information Matrix (FIM). For this purpose, a symbolic description of the joint FIM is derived. Its heterogeneous structure can make it badly conditioned, complicating computation of the determinant for D-optimal design. This problem is relaxed using determinant decomposition. A theoretical analysis and a case study show that experiment design for local model and partition parameters may significantly differ from each other.}, address = {Beijing, China}, author = {Kroll, Andreas and Dürrbaum, Axel}, booktitle = {Proceedings of the 17th IFAC Symposium on System Identification ({SysID})}, doi = {doi:10.1016/j.ifacol.2015.12.333}, interhash = {c8b8e8d219667b0df23b145097939fd4}, intrahash = {a62f4bcd0dac434df35ebba90261698d}, language = {english}, month = {October 19-21}, mrtnote = {peer,talk:Dürrbaum,oedg}, pages = {1427 -- 1432}, title = {On joint optimal experiment design for identifying partition and local model parameters of Takagi-Sugeno models}, year = 2015 } @inproceedings{rendle2009learning, abstract = {Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.}, address = {New York, NY, USA}, author = {Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, doi = {10.1145/1557019.1557100}, interhash = {1cc85ca2ec82db2a3caf40fd1795a58a}, intrahash = {1bd672ffb8d6ba5589bb0c7deca09412}, isbn = {978-1-60558-495-9}, location = {Paris, France}, pages = {727--736}, publisher = {ACM}, title = {Learning optimal ranking with tensor factorization for tag recommendation}, url = {http://portal.acm.org/citation.cfm?doid=1557019.1557100}, year = 2009 } @article{journals/tkde/BrunoW07, author = {Bruno, Nicolas and Wang, Hui}, date = {2007-06-05}, ee = {http://dx.doi.org/10.1109/TKDE.2007.1011}, interhash = {10808bc663c45993fb1f7fb6cf8cadd9}, intrahash = {e858b8ea4131ca83354910e12d26b389}, journal = {IEEE Trans. Knowl. Data Eng.}, number = 4, pages = {523-537}, title = {The Threshold Algorithm: From Middleware Systems to the Relational Engine.}, url = {http://dblp.uni-trier.de/db/journals/tkde/tkde19.html#BrunoW07}, volume = 19, year = 2007 } @inproceedings{citeulike:2801543, abstract = {: Assume that each object in a database has m grades, or scores, one for each of m attributes. For example, an object can have a color grade, that tells how red it is, and a shape grade, that tells how round it is. For each attribute, there is a sorted list, which lists each object and its grade under that attribute, sorted by grade (highest grade first). There is some monotone aggregation function, or combining rule, such as min or average, that combines the individual grades to obtain an...}, author = {Fagin, Ronald and Lotem, Amnon and Naor, Moni}, booktitle = {Symposium on Principles of Database Systems}, citeulike-article-id = {2801543}, interhash = {8bbc6d283a09e8ec8c082496b2f25865}, intrahash = {5fae1d60624767e4b3dea4d1a985cf7c}, posted-at = {2008-05-15 13:57:01}, priority = {0}, title = {Optimal Aggregation Algorithms for Middleware}, url = {http://citeseer.ist.psu.edu/441654.html}, year = 2001 }