Graph Embedded One-Class Classifiers for media data classification

Mygdalis, Vasileios; Iosifidis, Alexandros; Tefas, Anastasios; Pitas, Ioannis

doi:10.1016/j.patcog.2016.05.033

Cited by 42 publications

(23 citation statements)

References 39 publications

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“…As a prevalent boundary based OCC method, OCSVM has been studied and applied actively in numerous realms of academic research and industrial applications, such as fault detection [7], video abnormal event detection [8], media classification [9], network intrusion detection [10], video summarization [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter selection of one-class support vector machine by self-adaptive data shifting

Wang

Liu

Zhu

et al. 2018

Pattern Recognition

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With flexible data description ability, one-class Support Vector Machine (OCSVM) is one of the most popular and widely-used methods for one-class classification (OCC). Nevertheless, the performance of OCSVM strongly relies on its hyperparameter selection, which is still a challenging open problem due to the absence of outlier data. This paper proposes a fully automatic OCSVM hyperparameter selection method, which requires no tuning of additional hyperparameter, based on a novel self-adaptive "data shifting" mechanism: Firstly, by efficient edge pattern detection (EPD) and "negatively" shifting edge patterns along the negative direction of estimated data density gradient, a constrained number of high-quality pseudo outliers are self-adaptively generated at more desirable locations, which readily avoids two major difficulties in previous outlier generation methods. Secondly, to avoid time-consuming cross-validation and enhance robustness to noise in the given training data, a pseudo target set is generated for model validation by "positively" shifting each given target datum along the positive direction of data density gradient. Experiments on synthetic and benchmark datasets demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Hyperparameter selection of one-class support vector machine by self-adaptive data shifting

Wang

Liu

Zhu

et al. 2018

Pattern Recognition

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“…According to the techniques used in static novelty detection, one class classification techniques are those which are the most representative ones in this learning scenario. For instance, one class SVM (Dufrenois and Noyer, 2016;Erfani et al, 2016;Khreich et al, 2017), K-Nearest Neighbors data description (Tax, 2001), graph embedded one class classifiers (Mygdalis et al, 2016), one class Random Forests (Désir et al, 2013) and Isolation Forest (Zhang et al, Figure 5b). Finally, the new emerging class is sought.…”

Section: Novelty Detectionmentioning

confidence: 99%

Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

2019

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In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a mix-up between terminology and problems. In some research, similar problems have been named differently; while in some other works, the same term has been used to describe different problems. This confusion between terms and problems causes the repetition of research and hinders the advance of the field. Therefore, a standardization is imperative. The goal of this paper is to underline the differences between each term, and organize the area by looking at all these terms under the umbrella of supervised classification. Therefore, a one-to-one assignment of terms to learning scenarios is proposed. In fact, each learning scenario is associated with the term most frequently used in the literature. In order to validate this proposal, a set of experiments retrieving papers from Google Scholar, ACM Digital Library and IEEE Xplore has been carried out.

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“…For example in [44], [45] and [46], the ellipse shape is used for encapsulating target data instead of the traditional sphere used in SVDD. In [47] it is shown that both SVDD and OC-SVM lead to the same solution when exploiting the eliptical shape of the class. The last category of algorithms for improving one-class classifier performance attempts to handle non-stationary data.…”

Section: One-class Classificationmentioning

confidence: 99%

Subspace Support Vector Data Description

Sohrab

Raitoharju

Gabbouj

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. The data description in the new subspace is obtained by Support Vector Data Description. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulation. We conduct experiments on two multimodal datasets and compare the proposed approach with baseline and recently proposed one-class classification methods combined with early fusion and also considering each modality separately. We show that the proposed Multimodal Subspace Support Vector Data Description outperforms all the methods using data from a single modality and performs better or equally well than the methods fusing data from all modalities.

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Graph Embedded One-Class Classifiers for media data classification

Abstract: General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above.

Cited by 42 publications

References 39 publications

Hyperparameter selection of one-class support vector machine by self-adaptive data shifting

Hyperparameter selection of one-class support vector machine by self-adaptive data shifting

Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

Subspace Support Vector Data Description

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