Kernel Methods for Predictive Sequence Analysis

This tutorial is meant for a broad audience: Students, researchers, biologists and computer scientist interested in (a) an overview of general and efficient algorithms for statistical learning used in computational biology, (b) sequence kernels for the problems such as promoter or splice site detection. No specific knowledge will be required since the tutorial is self-contained and most fundamental concepts are introduced during the course

Preprocessing for classification of sparse data: application to trajectory recognition

International audienceOn one hand, sparse coding, which is widely used in signal processing, consists of representing signals as linear combinations of few elementary patterns selected from a dedicated dictionary. The output is a sparse vector containing few coding coefficients and is called sparse code. On the other hand, Multilayer Perceptron (MLP) is a neural network classification method that learns non linear borders between classes using labeled data examples. The MLP input data are vectors, usually normalized and preprocessed to minimize the inter-class correlation. This article acts as a link between sparse coding and MLP by converting sparse code into convenient vectors for MLP input. This original association assures in this way the classification of any sparse signals. Experimental results obtained by the whole process on trajectories data and comparisons to other methods show that this approach is efficient for signals classification

Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page

Extracting discriminative features for identifying abnormal sequences in one-class mode

This paper presents a novel framework for detecting abnormal sequences in an one-class setting (i.e., only normal data are available), which is applicable to various domains. Examples include intrusion detection, fault detection and speaker verification. Detecting abnormal sequences with only normal data presents several challenges for anomaly detection: the weak discrimination of normal and abnormal sequences; the unavailability of the abnormal data and other issues. Traditional model-based anomaly detection techniques can solve some of the above issues but with limited discrimination power (because of directly modeling the normal data). In order to enhance the discriminative power for anomaly detection, we turn to extracting discriminative features from the generative model based on the principle deducted from the corresponding theoretical analysis. Then a new anomaly detection framework is developed on top of that. The proposed approach firstly projects all the sequential data into a model-based equal length feature space (this is theoretically proven to have better discriminative power than the model itself), and then adopts a classifier learned from the transformed data to detect anomalies. Experimental evaluation on both the synthetic and real-world data shows that our proposed approach outperforms several anomaly detection baseline algorithms for sequential data. © 2013 IEEE

Signature features with the visibility transformation

The signature in rough path theory provides a graduated summary of a path through an examination of the effects of its increments. Inspired by recent developments of signature features in the context of machine learning, we explore a transformation that is able to embed the effect of the absolute position of the data stream into signature features. This unified feature is particularly effective for its simplifying role in allowing the signature feature set to accommodate nonlinear functions of absolute and relative values

Positive Definite Kernels in Machine Learning

This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions $\{k(x,\cdot),x\in\mathcal{X}\}$ associated with a kernel $k$ defined on a space $\mathcal{X}$. We discuss at length the construction of kernel functions that take advantage of well-known statistical models. We provide an overview of numerous data-analysis methods which take advantage of reproducing kernel Hilbert spaces and discuss the idea of combining several kernels to improve the performance on certain tasks. We also provide a short cookbook of different kernels which are particularly useful for certain data-types such as images, graphs or speech segments.Comment: draft. corrected a typo in figure