10.0.2.2: Created page with "{{StudentProjectTemplate |Summary=Nonlinear methods for accurate deviation detection (NOMAD) |Programme=Master (IT or EIS) |Keywords=Machine learning, research preparatory, de..."

2014-03-07T11:46:58Z

Created page with "{{StudentProjectTemplate |Summary=Nonlinear methods for accurate deviation detection (NOMAD) |Programme=Master (IT or EIS) |Keywords=Machine learning, research preparatory, de..."

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{{StudentProjectTemplate
|Summary=Nonlinear methods for accurate deviation detection (NOMAD)
|Programme=Master (IT or EIS)
|Keywords=Machine learning, research preparatory, deviation detection, unsupervised methods
|TimeFrame=The project should be done during the spring of 2014 and finished in late May. Ideally, the student(s) should start before the end of 2013.
|References=[1] Kriegel, Kröger, Zimek, "Outlier Detection Techniques", Tutorial, The 2010 SIAM International Conference on Data Mining, http://www.siam.org/meetings/sdm10/tutorial3.pdf
[2] Sudjianto, Nair, Yuan, Zhang, Kern, Cela-Díaz, "Statistical Methods for Fighting Financial Crimes", Technometrics, vol 52, pp 5-19 (2010)
[3] Schölkopf, Platt, Shawe-Taylor, Smola, Williamson, "Estimating the Support of a High-Dimensional Distribution", Neural Computation, vol 13, pp 1443–1471 (2001)
[4] Guo, Chena, Tsai, "A boundary method for outlier detection based on support vector domain description", Pattern Recognition, vol 42, pp 77-83 (2009)
|Prerequisites=Learning systems, multivariate analysis, programming skills
|Supervisor=Thorsteinn Rögnvaldsson, Stefan Byttner
|Examiner=Professor Antanas Verikas
|Level=Master
|Status=Open
}}
The setting is unsupervised detection of deviations in data. The initial research question is to explore the efficiency of a selected set of unsupervised deviation detection methods (of which at least one is a novel method). The project is software related. The student(s) must have experience with matlab, be skilled in mathematics and be able to work in an organized way.
Deliverables:
(a) A definition and collection of benchmark problems
(b) A state-of-the-art summary of suitable methods
(c) A set of results of selected methods on the benchmark problems
(d) An analysis and conclusion from these results
(e) A report
The work is well suited for writing a short scientific paper in the end and submit it to a conference. The project is suitable for 1-2 persons with high work capacity and the ambition to show abilities for scientific or high-level development work. The field is huge so there is no problem to find enough individual work for 2 students.

Example work packages:
(a) Define the problem and define what aspects that should be tested / explored.
(b) Literature search for state-of-the-art methods
(c) Implementation of selected methods for the study
(d) Run experiments
(e) Analyze and write report

NOMAD - Revision history

10.0.2.2: Created page with "{{StudentProjectTemplate |Summary=Nonlinear methods for accurate deviation detection (NOMAD) |Programme=Master (IT or EIS) |Keywords=Machine learning, research preparatory, de..."