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The Journal of Machine Learning Research 2012年第1期13卷

作者： Marius Kloft Pavel Laskov Machine Learing Group Technische Universität Berlin Berlin Germany and Department of Brain and Cognitive Engineering Korea University Seoul Korea Wilhelm-Schickard Institute for Computer Science University of Tübingen Tübingen Germany

Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). In such cases, learning algorithms may have to cope with manipulated data aimed at hampering decision making. Although some previous work addressed the issue of handling malicious data in the context of supervised learning, very little is known about the behavior of anomaly detection methods in such scenarios. In this contribution, we analyze the performance of a particular method--online centroid anomaly detection--in the presence of adversarial noise. Our analysis addresses the following security-related issues: formalization of learning and attack processes, derivation of an optimal attack, and analysis of attack efficiency and limitations. We derive bounds on the effectiveness of a poisoning attack against centroid anomaly detection under different conditions: attacker's full or limited control over the traffic and bounded false positive rate. Our bounds show that whereas a poisoning attack can be effectively staged in the unconstrained case, it can be made arbitrarily difficult (a strict upper bound on the attacker's gain) if external constraints are properly used. Our experimental evaluation, carried out on real traces of HTTP and exploit traffic, confirms the tightness of our theoretical bounds and the practicality of our protection mechanisms.

关键词： adversarial anomaly detection computer security network intrusion detection security analysis support vector data description

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Algebraic geometric comparison of probability distributions

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2012年第1期13卷

作者： Franz J. Király Paul Von Bünau Frank C. Meinecke Duncan A. J. Blythe Klaus-Robert Müller Machine Learning Group Computer Science Berlin Institute of Technology TU Berlin Berlin Germany and Institute of Mathematics FU Berlin Machine Learning Group Computer Science Berlin Institute of Technology TU Berlin Berlin Germany Machine Learning Group Computer Science Berlin Institute of Technology TU Berlin Berlin Germany and Bernstein Center for Computational Neuroscience Berlin Machine Learning Group Computer Science Berlin Institute of Technology TU Berlin Berlin Germany and Department of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-gu Seoul Korea

We propose a novel algebraic algorithmic framework for dealing with probability distributions represented by their cumulants such as the mean and covariance matrix. As an example, we consider the unsupervised learning problem of finding the subspace on which several probability distributions agree. Instead of minimizing an objective function involving the estimated cumulants, we show that by treating the cumulants as elements of the polynomial ring we can directly solve the problem, at a lower computational cost and with higher accuracy. Moreover, the algebraic viewpoint on probability distributions allows us to invoke the theory of algebraic geometry, which we demonstrate in a compact proof for an identifiability criterion.

关键词： approximate algebra computational algebraic geometry unsupervised Learning

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Latent fingerprint enhancement via robust orientation field estimation

Latent fingerprint enhancement via robust orientation field ...

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2011 International Joint Conference on Biometrics, IJCB 2011

作者： Yoon, Soweon Feng, Jianjiang Jain, Anil K. Dept. of Computer Science and Engineering Michigan State University United States Dept. of Automation Tsinghua University Beijing China Dept. of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-gu Seoul 136-713 Korea Republic of

ISBN: (纸本)9781457713583

Latent fingerprints, or simply latents, have been considered as cardinal evidence for identifying and convicting criminals. The amount of information available for identification from latents is often limited due to their poor quality, unclear ridge structure and occlusion with complex background or even other latent prints. We propose a latent fingerprint enhancement algorithm, which expects manually marked region of interest (ROI) and singular points. The core of the proposed algorithm is a robust orientation field estimation algorithm for latents. Short-time Fourier transform is used to obtain multiple orientation elements in each image block. This is followed by a hypothesize-and-test paradigm based on randomized RANSAC, which generates a set of hypothesized orientation fields. Experimental results on NIST SD27 latent fingerprint database show that the matching performance of a commercial matcher is significantly improved by utilizing the enhanced latent fingerprints produced by the proposed algorithm. © 2011 IEEE.

关键词： Image segmentation

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Time-Dependent Common Spatial Patterns Optimization for EEG Signal Classification

Time-Dependent Common Spatial Patterns Optimization for EEG ...

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第一届亚洲模式识别会议

作者： Tae-Eui Kam Seong-Whan Lee Dept. of Computer Science and Engineering Korea University Korea Dept. of Brain and Cognitive Engineering Korea University Korea

ISBN: (纸本)9781457701221

Recognizing Event-Related Desynchronization or Synchronization (ERD/ERS) patterns generated by motor imagery tasks is an important process in brain-computer interfaces (BCI). One of the most well-known algorithms to extract the discriminative patterns is Common Spatial Patterns (CSP). It finds an optimal spatial filter considering the spatial distribution of the ERD/ERS patterns. The CSP algorithm, however, does not consider temporal information of the Electroencephalogram (EEG) signals even though EEG signals are naturally non-stationary. In order to circumvent the limitation, in this paper, we propose a novel method, Time- Dependent Common Spatial Patterns (TDCSP) to classify multiclass motor imagery tasks. We optimize CSP filters in multiple local time ranges of EEG signals individually based on statistical analysis to effectively reflect changes of discriminative spatial distributions over time. We evaluated the proposed method by experiments on BCI Competition IV dataset 2-a, which resulted in high performance outperforming the previous methods in the literature.

关键词： brain-computer Interface (BCI) motor imagery electroencephalogram (EEG) Common Spatial Patterns (CSP) Time-Dependent Common Spatial Patterns (TDCSP)

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Classifying Categorical Data by Rule-Based Neighbors

Classifying Categorical Data by Rule-Based Neighbors

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IEEE International Conference on Data Mining (ICDM)

作者： Jiabing Wang Pei Zhang Guihua Wen Jia Wei School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Brain and Cognitive Science Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781457720758

A new learning algorithm for categorical data, named CRN (Classification by Rule-based Neighbors) is proposed in this paper. CRN is a nonmetric and parameter-free classifier, and can be regarded as a hybrid of rule induction and instance-based learning. Based on a new measure of attributes quality and the separate-and-conquer strategy, CRN learns a collection of feature sets such that for each pair of instances belonging to different classes, there is a feature set on which the two instances disagree. For an unlabeled instance I and a labeled instance I', I' is a neighbor of I if and only if they agree on all attributes of a feature set. Then, CRN classifies an unlabeled instance I based on I's neighbors on those learned feature sets. To validate the performance of CRN, CRN is compared with six state-of-the-art classifiers on twenty-four datasets. Experimental results demonstrate that although the underlying idea of CRN is simple, the predictive accuracy of CRN is comparable to or better than that of the state-of-the-art classifiers on most datasets.

关键词： Training Impurities Entropy Classification algorithms Measurement Accuracy

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Online multiple instance learning applied to hand detection in a humanoid robot

Online multiple instance learning applied to hand detection ...

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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Carlo Ciliberto Fabrizio Smeraldi Lorenzo Natale Giorgio Metta Robotics Brain and Cognitive Science Department Istituto Italiano di Tecnologia Genova Italy School of Electronic Engineering and Computer Science Queen Mary University of London UK

We propose an algorithm for the visual detection and localisation of the hand of a humanoid robot. This algorithm imposes low requirements on the type of supervision required to achieve good performance. In particular the system performs feature selection and adaptation using images that are only labelled as containing the hand or not, without any explicit segmentation. Our algorithm is an online variant of Multiple Instance Learning based on boosting. Experiments in real-world conditions on the iCub humanoid robot confirm that the algorithm can learn the visual appearance of the hand, reaching an accuracy comparable with its off-line version. This remains true when supervision is generated by the robot itself in a completely autonomous fashion. Algorithms with weak supervision requirements like the one we describe are useful for autonomous robots that learn and adapt online to a changing environment. The algorithm is not hand-specific and could be easily applied to wide range of problems involving visual recognition of generic objects.

关键词： Training Boosting Visualization Labeling Accuracy Humanoid robots

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Latent fingerprint matching using descriptor-based hough transform

Latent fingerprint matching using descriptor-based hough tra...

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2011 International Joint Conference on Biometrics, IJCB 2011

作者： Paulino, Alessandra A. Feng, Jianjiang Jain, Anil K. Dept. of Computer Science and Engineering Michigan State University East Lansing MI United States Dept. of Automation Tsinghua University Beijing China Dept. of Brain and Cognitive Engineering Korea University Anam-dong. Seongbuk-gu. Seoul 136-713 Korea Republic of

ISBN: (纸本)9781457713583

Identifying suspects based on impressions of fingers lifted from crime scenes (latent prints) is extremely important to law enforcement agencies. Latents are usually partial fingerprints with small area, contain nonlinear distortion, and are usually smudgy and blurred. Due to some of these characteristics, they have a significantly smaller number of minutiae points (one of the most important features in fingerprint matching) and therefore it can be extremely difficult to automatically match latents to plain or rolled fingerprints that are stored in law enforcement databases. Our goal is to develop a latent matching algorithm that uses only minutiae information. The proposed approach consists of following three modules: (i) align two sets of minutiae by using a descriptor-based Hough Transform;(ii) establish the correspondences between minutiae;and (iii) compute a similarity score. Experimental results on NIST SD27 show that the proposed algorithm outperforms a commercial fingerprint matcher. © 2011 IEEE.

关键词： Hough transforms

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3D to 2D fingerprints: Unrolling and distortion correction

3D to 2D fingerprints: Unrolling and distortion correction

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2011 International Joint Conference on Biometrics, IJCB 2011

作者： Zhao, Qijun Jain, Anil Abramovich, Gil Department of Computer Science and Engineering Michigan State University United States GE Global Research Korea University Anam-dong Seongbuk-gu Seoul 136-713 Korea Republic of Department of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-gu Seoul 136-713 Korea Republic of

ISBN: (纸本)9781457713583

Touchless 3D fingerprint sensors can capture both 3D depth information and albedo images of the finger surface. Compared with 2D fingerprint images acquired by traditional contact-based fingerprint sensors, the 3D fingerprints are generally free from the distortion caused by non-uniform pressure and undesirable motion of the finger. Several unrolling algorithms have been proposed for virtual rolling of 3D fingerprints to obtain 2D equivalent fingerprints, so that they can be matched with the legacy 2D fingerprint databases. However, available unrolling algorithms do not consider the impact of distortion that is typically present in the legacy 2D fingerprint images. In this paper, we conduct a comparative study of representative unrolling algorithms and propose an effective approach to incorporate distortion into the unrolling process. The 3D fingerprint database was acquired by using a 3D fingerprint sensor being developed by the General Electric Global Research. By matching the 2D equivalent fingerprints with the corresponding 2D fingerprints collected with a commercial contact-based fingerprint sensor, we show that the compatibility between the 2D unrolled fingerprints and the traditional contact-based 2D fingerprints is improved after incorporating the distortion into the unrolling process. © 2011 IEEE.

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Quasilinear Control Theory: An Overview

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IFAC Proceedings Volumes 2011年第1期44卷 10958-10963页

作者： ShiNung Ching Yongsoon Eun Pierre T. Kabamba Semyon M. Meerkov Department of Brain and Cognitive Science Massachusetts Institute of Technology Cambridge MA 02139 USA Xerox Research Center Webster Webster NY 14580 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 USA

Abstract This paper presents a complete set of methods for the performance analysis and design of closed loop systems with nonlinear actuators and sensors (so-called Linear Plant/Nonlinear Instrumentation, or LPNI, systems). As it turns out, these methods are quite similar to those in the linear case. Accordingly, the resulting methods are referred to as Quasilinear Control (QLC) Theory. Since the main analysis and design techniques of QLC are not too different from the well known linear control-theoretic methods, QLC can be viewed as a simple addition to the standard toolbox of control engineering practitioners and students alike.

关键词： Nonlinear actuators and sensors Stochastic linearization Constrained control

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Exploiting foveation in user-centred image fusion

Exploiting foveation in user-centred image fusion

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IEEE GCC Conference & Exhibition

作者： E. Basaeed M. Al-Mualla D. Vernon Department of Computer Engineering Khalifa University of Science Technology and Research Sharjah UAE Department of Robotics Brain and Cognitive Sciences Italian Institute of Technology Genoa Italy

The selection of image fusion techniques has always been a compromise between effectiveness and efficiency. In this paper, foveation (using log-polar transformation) is introduced to satisfy the real-time requirement of applications where the fused image is eventually presented to a human to interpret. Log-polar transformation achieves a data reduction without a subsequent loss in perceptual information. Thus, it can be used to reduce the execution time of any existing image fusion technique without modifying the technique itself. In this paper, the log-polar transformation is integrated with three widely-used image fusion techniques: image averaging, the Laplacian pyramid, and the Discrete Wavelet Transform. Then, the proposed fusion process is objectively evaluated using a set of established metrics; they are Mutual Information (MI), Xydas & Petrovic (Q AB/F ), and Piella & Heijmans (Q w ).

关键词： Image fusion Retina Laplace equations Pixel Spatial resolution Discrete wavelet transforms

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