Subspace Support Vector Data Description

Sohrab, Fahad; Raitoharju, Jenni; Gabbouj, Moncef; Iosifidis, Alexandros

doi:10.48550/arxiv.1802.03989

Cited by 3 publications

(7 citation statements)

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“…We have shown that the performance of EcoSVM is comparable to traditional SVMs that work with all data simultaneously. Our algorithm differs from previous online SVMs which must recompute the support vectors at each learning step [21][22][23][24][25][26] allowing for faster implementation and smaller memory requirements. While in the main text we focus on linearly separable data, as shown in the SI these same ideas can be generalized to non-separable data and for outlier detection (SVDD) using unlabeled data.…”

Section: Discussionmentioning

confidence: 99%

“…Because we use the ecologically inspired invasion condition, there is no need to recompute the support vectors at each learning step, resulting in a faster and more memory-efficient online algorithm than those that were previously suggested [21][22][23][24][25][26]. The EcoSVM algorithm also reduces the amount of training data that needs to be stored in memory.…”

Section: Ecosvm: An Online Algorithmmentioning

confidence: 99%

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Machine Learning as Ecology

Howell,

Wenping,

Marsland

et al. 2019

Preprint

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Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms -including Support Vector Machines (SVMs) -have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark performance using the MNIST dataset. Our work provides a new ecological lens through which we can view statistical learning and opens the possibility of designing ecosystems for machine learning.

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Section: Discussionmentioning

confidence: 99%

Section: Ecosvm: An Online Algorithmmentioning

confidence: 99%

Machine Learning as Ecology

Howell,

Wenping,

Marsland

et al. 2019

Preprint

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show abstract

“…If the condition is not satisfied, the point goes 'extinct' and the set of support vectors does not change. If a data point can invade successfully, the species abundances 'a i ' are modified and can be found by solving for the steady state of (7) using either forward integration, quadratic programming [8] or any other online SVM approximation scheme [22][23][24][25][26][27]. This suggests a simple new approximate algorithm for online SVM learning we term the EcoSVM.…”

Section: Ecosvm: An Online Svm Algorithmmentioning

confidence: 99%

“…Because we use the ecologically inspired invasion condition, there is no need to recompute the support vectors at each learning step, unlike the online SVM algorithms that were previously suggested [22][23][24][25][26][27]. The EcoSVM algorithm also reduces the amount of training data that needs to be stored in memory.…”

Section: Ecosvm: An Online Svm Algorithmmentioning

confidence: 99%

Machine learning as ecology

Howell¹,

Cui²,

Marsland³

et al. 2020

J. Phys. A: Math. Theor.

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Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms-including support vector machines (SVMs)-have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark performance using the MNIST dataset. Our work provides a new ecological lens through which we can view statistical learning and opens the possibility of designing ecosystems for machine learning.

show abstract

“…where, as in OC-SVM, the ξ's model the slack. There have been extensions of this scheme, such as the mSVDD that uses a mixture of such hyperspheres [29], density-induced SVDD [30], using kernelized variants [52], and more recently, to use subspaces for data description [49]. A major drawback of SVDD in general is the strong assumption it makes on the isotropic nature of the underlying data distribution.…”

Section: Background and Related Workmentioning

confidence: 99%

GODS: Generalized One-Class Discriminative Subspaces for Anomaly Detection

Wang

Cherian

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

101

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One-class learning is the classic problem of fitting a model to data for which annotations are available only for a single class. In this paper, we propose a novel objective for one-class learning. Our key idea is to use a pair of orthonormal frames -as subspaces -to "sandwich" the labeled data via optimizing for two objectives jointly: i) minimize the distance between the origins of the two subspaces, and ii) to maximize the margin between the hyperplanes and the data, either subspace demanding the data to be in its positive and negative orthant respectively. Our proposed objective however leads to a non-convex optimization problem, to which we resort to Riemannian optimization schemes and derive an efficient conjugate gradient scheme on the Stiefel manifold.To study the effectiveness of our scheme, we propose a new dataset Dash-Cam-Pose, consisting of clips with skeleton poses of humans seated in a car, the task being to classify the clips as normal or abnormal; the latter is when any human pose is out-of-position with regard to say an airbag deployment. Our experiments on the proposed Dash-Cam-Pose dataset, as well as several other standard anomaly/novelty detection benchmarks demonstrate the benefits of our scheme, achieving state-of-the-art oneclass accuracy. 5 The data follows the formula f (x) = √ x * (x + sign(randn) * rand), where randn and rand are standard MATLAB functions.

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Subspace Support Vector Data Description

Cited by 3 publications

References 0 publications

Machine Learning as Ecology

Machine Learning as Ecology

Machine learning as ecology

GODS: Generalized One-Class Discriminative Subspaces for Anomaly Detection

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