One-class slab support vector machine

Fragoso, Victor; Scheirer, Walter J.; Hespanha, João P.; Turk, Matthew

doi:10.1109/icpr.2016.7899670

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…B. We adopted the one vs rest [19] strategy which consists of considering a class c i ∈ C as a target one and the others as outliers [29,27,19, Australian 2 14 690 Diabetes 2 8 268 Ionosphere 2 34 351 Iris 3 4 150 Satimage 6 36 4435 Segment 7 19 2310 Table 2: Benchmark datasets [33,34] 30, 31,32]. In this case, the outliers injected in a given data subset were randomly picked among the representatives of the outlier classes, i.e., C\c i .…”

Section: Benchmark Datasetsmentioning

confidence: 99%

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A one-class classification decision tree based on kernel density estimation

Itani

Lecron

Fortemps

2020

Applied Soft Computing

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One-class Classification (OCC) is an area of machine learning which addresses prediction based on unbalanced datasets. Basically, OCC algorithms achieve training by means of a single class sample, with potentially some additional counter-examples. The current OCC models give satisfaction in terms of performance, but there is an increasing need for the development of interpretable models. In the present work, we propose a one-class model which addresses concerns of both performance and interpretability. Our hybrid OCC method relies on density estimation as part of a tree-based learning algorithm, called One-Class decision Tree (OC-Tree). Within a greedy and recursive approach, our proposal rests on kernel density estimation to split a data subset on the basis of one or several intervals of interest. Thus, the OC-Tree encloses data within hyper-rectangles of interest which can be described by a set of rules. Against state-of-the-art methods such as Cluster Support Vector Data Description (Clus-terSVDD), One-Class Support Vector Machine (OCSVM) and isolation Forest (iForest), the OC-Tree performs favorably on a range of benchmark datasets. Furthermore, we propose a real medical application for which the OC-Tree has demonstrated its effectiveness, through the ability to tackle interpretable diag- * Corresponding author.

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Section: Benchmark Datasetsmentioning

confidence: 99%

“…Benchmark datasets[33,34] 30,31,32]. In this case, the outliers injected in a given data subset were randomly picked among the representatives of the outlier classes, i.e., C\c i .…”

mentioning

confidence: 99%

A one-class classification decision tree based on kernel density estimation

Itani

Lecron

Fortemps

2020

Applied Soft Computing

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“…We first present a simplified variant of this objective by using two different (sets of) hyperplanes, dubbed Basic One-class Discriminative Subspaces (BODS), that can sandwich the labeled data by bounding from different sides; these hyperplanes are independently parameterized and thus can be oriented differently to better fit to the labeled data. Note that there is a similar prior work, termed Slab-SVM [18], that learns two hyperplanes for one-class classification. However, these hyperplanes are constrained to have the same slope, which we do not impose in our BODS model, as a result, our model is more general than Slab-SVM.…”

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)

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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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“…2(a)); these hyperplanes are independently parameterized and bounds the data distribution. Note that there is a similar prior work, termed Slab-SVM [89], that learns two hyperplanes for one-class classification. However, their hyperplanes are constrained to have the same slope, which we do not impose in our BODS model; as a result, our model is more flexible than Slab-SVM.…”

Section: Introductionmentioning

confidence: 99%

Generalized One-Class Learning Using Pairs of Complementary Classifiers

Cherian

Wang

2021

Preprint

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One-class learning is the classic problem of fitting a model to the data for which annotations are available only for a single class. In this paper, we explore novel objectives for one-class learning, which we collectively refer to as Generalized One-class Discriminative Subspaces (GODS). Our key idea is to learn a pair of complementary classifiers to flexibly bound the one-class data distribution, where the data belongs to the positive half-space of one of the classifiers in the complementary pair and to the negative half-space of the other. To avoid redundancy while allowing non-linearity in the classifier decision surfaces, we propose to design each classifier as an orthonormal frame and seek to learn these frames via jointly optimizing for two conflicting objectives, namely: i) to minimize the distance between the two frames, and ii) to maximize the margin between the frames and the data. The learned orthonormal frames will thus characterize a piecewise linear decision surface that allows for efficient inference, while our objectives seek to bound the data within a minimal volume that maximizes the decision margin, thereby robustly capturing the data distribution. We explore several variants of our formulation under different constraints on the constituent classifiers, including kernelized feature maps. We demonstrate the empirical benefits of our approach via experiments on data from several applications in computer vision, such as anomaly detection in video sequences, human poses, and human activities. We also explore the generality and effectiveness of GODS for non-vision tasks via experiments on several UCI datasets, demonstrating state-of-the-art results.

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One-class slab support vector machine

Cited by 7 publications

References 22 publications

A one-class classification decision tree based on kernel density estimation

A one-class classification decision tree based on kernel density estimation

GODS: Generalized One-Class Discriminative Subspaces for Anomaly Detection

Generalized One-Class Learning Using Pairs of Complementary Classifiers

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