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With the aim of achieving a computationally efficient optimization of kernel-based probabilistic models for various problems, such as sequential pattern recognition, we have already developed the kernel gradient matching pursuit method as an approximation technique for kernel-based classification. The conventional kernel gradient matching pursuit method approximates the optimal parameter vector by...
Adaptive online algorithms for simultaneously extracting nonlinear eigenvectors of kernel principal component analysis (KPCA) are developed. KPCA needs all the observed samples to represent basis functions, and the same scale of eigenvalue problem as the number of samples should be solved. This paper reformulates KPCA and deduces an expression in the Euclidean space, where an algorithm for tracking...
Edge-preserving smoothing is widely used in image processing and bilateral filtering is one way to achieve it. Bilateral filter is a nonlinear combination of domain and range filters. Implementing the classical bilateral filter is computationally intensive, owing to the nonlinearity of the range filter. In the standard form, the domain and range filters are Gaussian functions and the performance depends...
Music recommendation is an important aspect of many streaming services and multi-media systems, however, it is typically based on so-called collaborative filtering methods. In this paper we consider the recommendation task from a personal viewpoint and examine to which degree music preference can be elicited and predicted using simple and robust queries such as pairwise comparisons. We propose to...
Intrusion detection datasets play a major role in evaluating machine learning techniques for Intrusion Detection Systems. The Intrusion detection datasets are generally very large and contain many noncontributing features and redundant data. These drawbacks lead to inaccurate intrusion detection and increased computational cost when machine learning techniques are evaluated. Several data cleaning...
Target counting in sensor-based surveillance systems is an interesting task that potentially could have many important applications in practice. In such a system, each sensor outputs the number of targets in its sensing region, and the problem is how one can combine all the reported numbers from sensors to provide an estimate of the total number of targets present in the entire monitored area. The...
Standard SVM training has O(m3) time and O(m2) space complexities, where m is the training set size. It is thus computationally infeasible on very large data sets. The author first reviewed the standard minimum enclosing ball (MEB) problems in computational geometry and presented the extensions of minimum enclosing ball problem, Then proposed a novel SVM algorithm-extension core vector machine algorithm...
Since 1985, the IEEE 754 standard defines formats, rounding modes and basic operations for floating-point arithmetic. In 2008 the standard has been extended, and recommendations have been added about the rounding of some elementary functions such as trigonometric functions (cosine, sine, tangent and their inverses), exponentials, and logarithms. However to guarantee the exact rounding of these functions...
In this paper, we propose a compact image signature based on VLAT. Our method integrates spatial information while significantly reducing the size of original VLAT by using two pojection steps. we carry out experiments showing our approach is competitive with state of the art signatures.
The large amount of digital data requests for scalable tools like efficient clustering algorithms. Many algorithms for large data sets request linear separability in an Euclidean space. Kernel approaches can capture the non-linear structure but do not scale well for large data sets. Alternatively, data are often represented implicitly by dissimilarities like for protein sequences, whose methods also...
Visual object tracking in video can be formulated as a time varying appearance-based binary classification problem. Tracking algorithms need to adapt to changes in both foreground object appearance as well as varying scene backgrounds. Fusing information from multimodal features (views or representations) typically enhances classification performance without increasing classifier complexity when image...
This paper describes a method of tracking multiple persons with occlusions using stereo. We previously developed an accurate and stable tracking method using overlapping silhouette templates which considers how persons overlap in the image. It realized a fast tracking by using an approximated likelihood map based on kernel density estimation. The method, however, treated only two overlapping persons...
In this paper, a low complexity system for spectral analysis of heart rate variability (HRV) is presented. The main idea of the proposed approach is the implementation of the Fast-Lomb periodogram that is a ubiquitous tool in spectral analysis, using a wavelet based Fast Fourier transform. Interestingly we show that the proposed approach enables the classification of processed data into more and less...
From the beginning of the data analysis system cluster computing plays an important role on it. The very early developed clustering algorithms which can handle only numerical data and K-means clustering is one of them and was proposed by Macqueen [1] in 1967. This algorithm helps us to find the homogeneity of the data set. This K-means algorithm has been modified in many ways to get the modified K-means...
This paper describes a problem of distribution free learning theory wherein a collection of n agents make independent and identically distributed observations of an unknown function and seek consensus in their construction of an optimal estimate. The learning objective is expressed in terms of an error defined over a reproducing kernel Hilbert spaces (RKHS) and in terms of a multiplier that enforces...
In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact representations but with limited ability to represent deformation across...
This paper considers the problem of bearings only tracking of manoeuvring targets. A learning particle filtering algorithm is proposed which can estimate both the unknown target states and unknown model parameters. The algorithm performance is validated and tested over a challenging scenario with abrupt manoeuvres. A comparison of the proposed algorithm with the Interacting Multiple Model (IMM) filter...
We propose a new importance sampling method that constructs an importance sampling density which approximates the zero-variance sampling density nonparametrically as follows. In a first stage, it generates a sample (possibly approximately) from the zero-variance density using, for example, Markov chain Monte Carlo methodology. In a second stage, the method constructs a kernel density estimator of...
In this work we present a quadratic programming approximation of the Semi-Supervised Support Vector Machine (S3VM) problem, namely approximate QP-S3VM, that can be efficiently solved using off the shelf optimization packages. We prove that this approximate formulation establishes a relation between the low density separation and the graph-based models of semi-supervised learning (SSL) which is important...
The continual reachability set, the set of initial states of a constrained dynamical system that can reach a target at any desired time, is introduced. The properties of this set are investigated and its connection with maximal reachability constructs is examined. Owing to this connection, efficient and scalable maximal reachability techniques can be used to compute the continual reachability set...
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