7,773 research outputs found
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
We present a novel model called OCGAN for the classical problem of one-class
novelty detection, where, given a set of examples from a particular class, the
goal is to determine if a query example is from the same class. Our solution is
based on learning latent representations of in-class examples using a denoising
auto-encoder network. The key contribution of our work is our proposal to
explicitly constrain the latent space to exclusively represent the given class.
In order to accomplish this goal, firstly, we force the latent space to have
bounded support by introducing a tanh activation in the encoder's output layer.
Secondly, using a discriminator in the latent space that is trained
adversarially, we ensure that encoded representations of in-class examples
resemble uniform random samples drawn from the same bounded space. Thirdly,
using a second adversarial discriminator in the input space, we ensure all
randomly drawn latent samples generate examples that look real. Finally, we
introduce a gradient-descent based sampling technique that explores points in
the latent space that generate potential out-of-class examples, which are fed
back to the network to further train it to generate in-class examples from
those points. The effectiveness of the proposed method is measured across four
publicly available datasets using two one-class novelty detection protocols
where we achieve state-of-the-art results.Comment: CVPR 2019 Accepted Pape
Network Traffic Anomaly Detection
This paper presents a tutorial for network anomaly detection, focusing on
non-signature-based approaches. Network traffic anomalies are unusual and
significant changes in the traffic of a network. Networks play an important
role in today's social and economic infrastructures. The security of the
network becomes crucial, and network traffic anomaly detection constitutes an
important part of network security. In this paper, we present three major
approaches to non-signature-based network detection: PCA-based, sketch-based,
and signal-analysis-based. In addition, we introduce a framework that subsumes
the three approaches and a scheme for network anomaly extraction. We believe
network anomaly detection will become more important in the future because of
the increasing importance of network security.Comment: 26 page
Multi-scale streaming anomalies detection for time series
In the class of streaming anomaly detection algorithms for univariate time
series, the size of the sliding window over which various statistics are
calculated is an important parameter. To address the anomalous variation in the
scale of the pseudo-periodicity of time series, we define a streaming
multi-scale anomaly score with a streaming PCA over a multi-scale lag-matrix.
We define three methods of aggregation of the multi-scale anomaly scores. We
evaluate their performance on Yahoo! and Numenta dataset for unsupervised
anomaly detection benchmark. To the best of authors' knowledge, this is the
first time a multi-scale streaming anomaly detection has been proposed and
systematically studied.Comment: 10 pages, two columns, Accepted at Conference d'Apprentissage 2017
Grenobl
Anomaly-Sensitive Dictionary Learning for Unsupervised Diagnostics of Solid Media
This paper proposes a strategy for the detection and triangulation of
structural anomalies in solid media. The method revolves around the
construction of sparse representations of the medium's dynamic response,
obtained by learning instructive dictionaries which form a suitable basis for
the response data. The resulting sparse coding problem is recast as a modified
dictionary learning task with additional spatial sparsity constraints enforced
on the atoms of the learned dictionaries, which provides them with a prescribed
spatial topology that is designed to unveil anomalous regions in the physical
domain. The proposed methodology is model agnostic, i.e., it forsakes the need
for a physical model and requires virtually no a priori knowledge of the
structure's material properties, as all the inferences are exclusively informed
by the data through the layers of information that are available in the
intrinsic salient structure of the material's dynamic response. This
characteristic makes the approach powerful for anomaly identification in
systems with unknown or heterogeneous property distribution, for which a model
is unsuitable or unreliable. The method is validated using both syntheticallyComment: Submitted to the Proceedings of the Royal Society
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
We propose a neural network for unsupervised anomaly detection with a novel
robust subspace recovery layer (RSR layer). This layer seeks to extract the
underlying subspace from a latent representation of the given data and removes
outliers that lie away from this subspace. It is used within an autoencoder.
The encoder maps the data into a latent space, from which the RSR layer
extracts the subspace. The decoder then smoothly maps back the underlying
subspace to a "manifold" close to the original inliers. Inliers and outliers
are distinguished according to the distances between the original and mapped
positions (small for inliers and large for outliers). Extensive numerical
experiments with both image and document datasets demonstrate state-of-the-art
precision and recall.Comment: This work is on the ICLR 2020 conferenc
Landmark Diffusion Maps (L-dMaps): Accelerated manifold learning out-of-sample extension
Diffusion maps are a nonlinear manifold learning technique based on harmonic
analysis of a diffusion process over the data. Out-of-sample extensions with
computational complexity , where is the number of points
comprising the manifold, frustrate applications to online learning applications
requiring rapid embedding of high-dimensional data streams. We propose landmark
diffusion maps (L-dMaps) to reduce the complexity to , where is the number of landmark points selected using pruned spanning trees or
k-medoids. Offering speedups in out-of-sample extension, L-dMaps
enables the application of diffusion maps to high-volume and/or high-velocity
streaming data. We illustrate our approach on three datasets: the Swiss roll,
molecular simulations of a CH polymer chain, and biomolecular
simulations of alanine dipeptide. We demonstrate up to 50-fold speedups in
out-of-sample extension for the molecular systems with less than 4% errors in
manifold reconstruction fidelity relative to calculations over the full
dataset.Comment: Submitte
Compressed Anomaly Detection with Multiple Mixed Observations
We consider a collection of independent random variables that are identically
distributed, except for a small subset which follows a different, anomalous
distribution. We study the problem of detecting which random variables in the
collection are governed by the anomalous distribution. Recent work proposes to
solve this problem by conducting hypothesis tests based on mixed observations
(e.g. linear combinations) of the random variables. Recognizing the connection
between taking mixed observations and compressed sensing, we view the problem
as recovering the "support" (index set) of the anomalous random variables from
multiple measurement vectors (MMVs). Many algorithms have been developed for
recovering jointly sparse signals and their support from MMVs. We establish the
theoretical and empirical effectiveness of these algorithms at detecting
anomalies. We also extend the LASSO algorithm to an MMV version for our
purpose. Further, we perform experiments on synthetic data, consisting of
samples from the random variables, to explore the trade-off between the number
of mixed observations per sample and the number of samples required to detect
anomalies.Comment: 27 pages, 9 figures. Incorporates reviewer feedback, additional
experiments, and additional figure
Change Detection with Compressive Measurements
Quickest change point detection is concerned with the detection of
statistical change(s) in sequences while minimizing the detection delay subject
to false alarm constraints. In this paper, the problem of change point
detection is studied when the decision maker only has access to compressive
measurements. First, an expression for the average detection delay of
Shiryaev's procedure with compressive measurements is derived in the asymptotic
regime where the probability of false alarm goes to zero. Second, the
dependence of the delay on the compression ratio and the signal to noise ratio
is explicitly quantified. The ratio of delays with and without compression is
studied under various sensing matrix constructions, including Gaussian
ensembles and random projections. For a target ratio of the delays after and
before compression, a sufficient condition on the number of measurements
required to meet this objective with prespecified probability is derived
Machine learning based hyperspectral image analysis: A survey
Hyperspectral sensors enable the study of the chemical properties of scene
materials remotely for the purpose of identification, detection, and chemical
composition analysis of objects in the environment. Hence, hyperspectral images
captured from earth observing satellites and aircraft have been increasingly
important in agriculture, environmental monitoring, urban planning, mining, and
defense. Machine learning algorithms due to their outstanding predictive power
have become a key tool for modern hyperspectral image analysis. Therefore, a
solid understanding of machine learning techniques have become essential for
remote sensing researchers and practitioners. This paper reviews and compares
recent machine learning-based hyperspectral image analysis methods published in
literature. We organize the methods by the image analysis task and by the type
of machine learning algorithm, and present a two-way mapping between the image
analysis tasks and the types of machine learning algorithms that can be applied
to them. The paper is comprehensive in coverage of both hyperspectral image
analysis tasks and machine learning algorithms. The image analysis tasks
considered are land cover classification, target detection, unmixing, and
physical parameter estimation. The machine learning algorithms covered are
Gaussian models, linear regression, logistic regression, support vector
machines, Gaussian mixture model, latent linear models, sparse linear models,
Gaussian mixture models, ensemble learning, directed graphical models,
undirected graphical models, clustering, Gaussian processes, Dirichlet
processes, and deep learning. We also discuss the open challenges in the field
of hyperspectral image analysis and explore possible future directions
One-class classification with application to forensic analysis
The analysis of broken glass is forensically important to reconstruct the
events of a criminal act. In particular, the comparison between the glass
fragments found on a suspect (recovered cases) and those collected on the crime
scene (control cases) may help the police to correctly identify the
offender(s). The forensic issue can be framed as a one-class classification
problem. One-class classification is a recently emerging and special
classification task, where only one class is fully known (the so-called target
class), while information on the others is completely missing. We propose to
consider classic Gini's transvariation probability as a measure of typicality,
i.e. a measure of resemblance between an observation and a set of well-known
objects (the control cases). The aim of the proposed Transvariation-based
One-Class Classifier (TOCC) is to identify the best boundary around the target
class, that is, to recognise as many target objects as possible while rejecting
all those deviating from this class
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