19,015 research outputs found
Calibration of One-Class SVM for MV set estimation
A general approach for anomaly detection or novelty detection consists in
estimating high density regions or Minimum Volume (MV) sets. The One-Class
Support Vector Machine (OCSVM) is a state-of-the-art algorithm for estimating
such regions from high dimensional data. Yet it suffers from practical
limitations. When applied to a limited number of samples it can lead to poor
performance even when picking the best hyperparameters. Moreover the solution
of OCSVM is very sensitive to the selection of hyperparameters which makes it
hard to optimize in an unsupervised setting. We present a new approach to
estimate MV sets using the OCSVM with a different choice of the parameter
controlling the proportion of outliers. The solution function of the OCSVM is
learnt on a training set and the desired probability mass is obtained by
adjusting the offset on a test set to prevent overfitting. Models learnt on
different train/test splits are then aggregated to reduce the variance induced
by such random splits. Our approach makes it possible to tune the
hyperparameters automatically and obtain nested set estimates. Experimental
results show that our approach outperforms the standard OCSVM formulation while
suffering less from the curse of dimensionality than kernel density estimates.
Results on actual data sets are also presented.Comment: IEEE DSAA' 2015, Oct 2015, Paris, Franc
A survey of kernel and spectral methods for clustering
Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods. The aim of this paper is to present a survey of kernel and spectral clustering methods, two approaches able to produce nonlinear separating hypersurfaces between clusters. The presented kernel clustering methods are the kernel version of many classical clustering algorithms, e.g., K-means, SOM and neural gas. Spectral clustering arise from concepts in spectral graph theory and the clustering problem is configured as a graph cut problem where an appropriate objective function has to be optimized. An explicit proof of the fact that these two paradigms have the same objective is reported since it has been proven that these two seemingly different approaches have the same mathematical foundation. Besides, fuzzy kernel clustering methods are presented as extensions of kernel K-means clustering algorithm. (C) 2007 Pattem Recognition Society. Published by Elsevier Ltd. All rights reserved
One-Class Support Measure Machines for Group Anomaly Detection
We propose one-class support measure machines (OCSMMs) for group anomaly
detection which aims at recognizing anomalous aggregate behaviors of data
points. The OCSMMs generalize well-known one-class support vector machines
(OCSVMs) to a space of probability measures. By formulating the problem as
quantile estimation on distributions, we can establish an interesting
connection to the OCSVMs and variable kernel density estimators (VKDEs) over
the input space on which the distributions are defined, bridging the gap
between large-margin methods and kernel density estimators. In particular, we
show that various types of VKDEs can be considered as solutions to a class of
regularization problems studied in this paper. Experiments on Sloan Digital Sky
Survey dataset and High Energy Particle Physics dataset demonstrate the
benefits of the proposed framework in real-world applications.Comment: Conference on Uncertainty in Artificial Intelligence (UAI2013
Multimodal Subspace Support Vector Data Description
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. We also propose
different regularization strategies for the proposed method and provide both
linear and non-linear formulations. The proposed Multimodal Subspace Support
Vector Data Description outperforms all the competing methods using data from a
single modality or fusing data from all modalities in four out of five
datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary
material (27 tables, 10 figures). The manuscript and supplementary material
are combined as a single .pdf (50 pages) fil
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