Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection

• URL: http://arxiv.org/abs/2009.12196v1
• Date: Thu, 24 Sep 2020 12:25:43 GMT
• Title: Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection
• Authors: Kai Ming Ting, Bi-Cun Xu, Takashi Washio and Zhi-Hua Zhou
• Abstract summary: Isolation Distributional Kernel (IDK) is a new way to measure the similarity between two distributions. We demonstrate IDK's efficacy and efficiency as a new tool for kernel based anomaly detection for both point and group anomalies.
• Score: 76.1522587605852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. Existing approaches based on kernel mean embedding, which convert a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is {\em data independent}. This paper shows that Isolation Distributional Kernel (IDK), which is based on a {\em data dependent} point kernel, addresses both key issues. We demonstrate IDK's efficacy and efficiency as a new tool for kernel based anomaly detection for both point and group anomalies. Without explicit learning, using IDK alone outperforms existing kernel based point anomaly detector OCSVM and other kernel mean embedding methods that rely on Gaussian kernel. For group anomaly detection,we introduce an IDK based detector called IDK$^2$. It reformulates the problem of group anomaly detection in input space into the problem of point anomaly detection in Hilbert space, without the need for learning. IDK$^2$ runs orders of magnitude faster than group anomaly detector OCSMM.We reveal for the first time that an effective kernel based anomaly detector based on kernel mean embedding must employ a characteristic kernel which is data dependent.

Related papers

• Multiple Kernel Clustering via Local Regression Integration [4.856913393644719]
  Multiple kernel methods less consider the intrinsic manifold structure of multiple kernel data. This paper first presents the clustering method via kernelized local regression (CKLR) We then extend it to perform clustering via the multiple kernel local regression (CMKLR)
  arXiv  Detail & Related papers  (2024-10-20T06:26:29Z)
• Variational Autoencoder Kernel Interpretation and Selection for Classification [59.30734371401315]
  This work proposed kernel selection approaches for probabilistic classifiers based on features produced by the convolutional encoder of a variational autoencoder. In the proposed implementation, each latent variable was sampled from the distribution associated with a single kernel of the last encoder's convolution layer, as an individual distribution was created for each kernel. choosing relevant features on the sampled latent variables makes it possible to perform kernel selection, filtering the uninformative features and kernels.
  arXiv  Detail & Related papers  (2022-09-10T17:22:53Z)
• Multiple Kernel Clustering with Dual Noise Minimization [56.009011016367744]
  Multiple kernel clustering (MKC) aims to group data by integrating complementary information from base kernels. In this paper, we rigorously define dual noise and propose a novel parameter-free MKC algorithm by minimizing them. We observe that dual noise will pollute the block diagonal structures and incur the degeneration of clustering performance, and C-noise exhibits stronger destruction than N-noise.
  arXiv  Detail & Related papers  (2022-07-13T08:37:42Z)
• Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph [73.68184322526338]
  Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. This paper proposes a novel local sample-weighted multiple kernel clustering model. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algo-rithms.
  arXiv  Detail & Related papers  (2022-07-05T05:00:38Z)
• Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics [0.4588028371034406]
  We introduce algorithms to select/design kernels in Gaussian process regression/kriging surrogate modeling techniques. A first class of algorithms is kernel flow, which was introduced in a context of classification in machine learning. A second class of algorithms is called spectral kernel ridge regression, and aims at selecting a "best" kernel such that the norm of the function to be approximated is minimal.
  arXiv  Detail & Related papers  (2022-06-03T07:50:54Z)
• Taming Nonconvexity in Kernel Feature Selection---Favorable Properties of the Laplace Kernel [77.73399781313893]
  A challenge is to establish the objective function of kernel-based feature selection. The gradient-based algorithms available for non-global optimization are only able to guarantee convergence to local minima.
  arXiv  Detail & Related papers  (2021-06-17T11:05:48Z)
• Kernel k-Means, By All Means: Algorithms and Strong Consistency [21.013169939337583]
  Kernel $k$ clustering is a powerful tool for unsupervised learning of non-linear data. In this paper, we generalize results leveraging a general family of means to combat sub-optimal local solutions. Our algorithm makes use of majorization-minimization (MM) to better solve this non-linear separation problem.
  arXiv  Detail & Related papers  (2020-11-12T16:07:18Z)
• The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms [12.363083467305787]
  Agglomerative hierarchical clustering (AHC) is one of the popular clustering approaches. Existing AHC methods, which are based on a distance measure, have difficulty in identifying adjacent clusters with varied densities. We show that the use of a data-dependent kernel (instead of distance or existing kernel) provides an effective means to address it.
  arXiv  Detail & Related papers  (2020-10-12T06:18:38Z)
• Manifold Adaptive Multiple Kernel K-Means for Clustering [5.8671688602326215]
  We adopt the manifold adaptive kernel, instead of the original kernel, to integrate the local manifold structure of kernels. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets.
  arXiv  Detail & Related papers  (2020-09-30T02:07:53Z)
• Learning Deep Kernels for Non-Parametric Two-Sample Tests [50.92621794426821]
  We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels parameterized by deep neural nets, trained to maximize test power.
  arXiv  Detail & Related papers  (2020-02-21T03:54:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.