Added missing citations

Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens 2019-09-09 14:14:11 +02:00
parent d165299df8
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2 changed files with 65 additions and 5 deletions

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@ -188,9 +188,9 @@ allows a probabilistic interpretation. Pidhorskyi et al.~\cite{Pidhorskyi2018}
combine a probabilistic approach to novelty detection with auto-encoders.
Distance-based novelty detection uses either nearest neighbour-based approaches
(e.g. ) %TODO citations)
(e.g. \(k\)-nearest neighbour \cite{Hautamaki2004})
or clustering-based approaches
(e.g. ). % TODO citations
(e.g. \(k\)-means clustering algorithm \cite{Jordan1994}).
Both methods are similar to estimating the
pdf of data, they use well-defined distance metrics to compute the distance
between two data points.
@ -198,7 +198,7 @@ between two data points.
Domain-based novelty detection describes the boundary of the known data, rather
than the data itself. Unknown data is identified by its position relative to
the boundary. A common implementation for this are support vector machines
(e.g. implemented by ). % TODO citations
(e.g. implemented by Song et al. \cite{Song2002}).
Information-theoretic novelty detection computes the information content
of a data set, for example, with metrics like entropy. Such metrics assume
@ -206,8 +206,8 @@ that novel data inside the data set significantly alters the information
content of an otherwise normal data set. First, the metrics are calculated over the
whole data set. Afterwards, a subset is identified that causes the biggest
difference in the metric when removed from the data set. This subset is considered
to consist of novel data. For example, xyz provide a recent approach.
% TODO citations
to consist of novel data. For example, Filippone and Sanguinetti \cite{Filippone2011} provide
a recent approach.
\subsection{Reconstruction-based novelty detection}

60
ma.bib
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@ -849,4 +849,64 @@ to construct explicit models for non-normal classes. Application includes infere
timestamp = {2019.08.27},
}
@InProceedings{Hautamaki2004,
author = {V. Hautamaki and I. Karkkainen and P. Franti},
title = {Outlier detection using k-nearest neighbour graph},
booktitle = {{ICPR} 2004},
year = {2004},
publisher = {{IEEE}},
doi = {10.1109/icpr.2004.1334558},
abstract = {We present an outlier detection using indegree number (ODIN) algorithm that utilizes k-nearest neighbour graph. Improvements to existing kNN distance-based method are also proposed. We compare the methods with real and synthetic datasets. The results show that the proposed method achieves reasonable results with synthetic data and outperforms compared methods with real data sets with small number of observations.},
file = {:/home/jim/Documents/Studium/MA/Literatur/58_k-nearest-neighbour.pdf:PDF},
owner = {jim},
timestamp = {2019.09.09},
}
@Article{Jordan1994,
author = {Michael I. Jordan and Robert A. Jacobs},
title = {Hierarchical Mixtures of Experts and the {EM} Algorithm},
journal = {Neural Computation},
year = {1994},
volume = {6},
number = {2},
pages = {181--214},
doi = {10.1162/neco.1994.6.2.181},
file = {:/home/jim/Documents/Studium/MA/Literatur/59_k-means.pdf:PDF},
owner = {jim},
publisher = {{MIT} Press - Journals},
timestamp = {2019.09.09},
}
@Article{Song2002,
author = {Qing Song and Wenjie Hu and Wenfang Xie},
title = {Robust support vector machine with bullet hole image classification},
journal = {{IEEE} Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)},
year = {2002},
volume = {32},
number = {4},
pages = {440--448},
doi = {10.1109/tsmcc.2002.807277},
file = {:/home/jim/Documents/Studium/MA/Literatur/60_robust-support-vector-machine.pdf:PDF},
owner = {jim},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
timestamp = {2019.09.09},
}
@Article{Filippone2011,
author = {Maurizio Filippone and Guido Sanguinetti},
title = {A Perturbative Approach to Novelty Detection in Autoregressive Models},
journal = {{IEEE} Transactions on Signal Processing},
year = {2011},
volume = {59},
number = {3},
month = {mar},
pages = {1027--1036},
doi = {10.1109/tsp.2010.2094609},
abstract = {We propose a new method to perform novelty detection in dynamical systems governed by linear autoregressive models. The method is based on a perturbative expansion to a statistical test whose leading term is the classical F-test, and whose O(1/n) correction can be approximated as a function of the number of training points and the model order alone. The method can be justified as an approximation to an information theoretic test. We demonstrate on several synthetic examples that the first correction to the F-test can dramatically improve the control over the false positive rate of the system. We also test the approach on some real time series data, demonstrating that the method still retains a good accuracy in detecting novelties.},
file = {:/home/jim/Documents/Studium/MA/Literatur/61_perturbative-approach-to-novelty-detection-autoregressive-models.pdf:PDF},
owner = {jim},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
timestamp = {2019.09.09},
}
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