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Multiple kernel learning methods combine a set of base kernels to produce an optimal one for a certain classification or regression problem. But selecting a set of base kernels from a plethora of kernels is not automated. We provide a criteria to select efficient base kernels. Automating the selection process of efficient base kernel requires less time and effort than manually selecting them. However,...
The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the...
Kernel methods represent some of the most popular machine learning tools for data analysis. Since exact kernel methods can be prohibitively expensive for large problems, reliable low-rank matrix approximations and high-performance implementations have become indispensable for practical applications of kernel methods. In this work, we introduce spectrum-revealing Cholesky factorization, a reliable...
Stereo matching methods estimate depth information of captured images. One way to estimate accurate depth values is to use the distance information. This method enhances the disparity map by preserving the edge region. In order to preserve the depth discontinuity near the edge region, it uses the distance information as a new weighting value for the matching cost function. However, this method has...
The combined attacks are widely spread in the domain of smart cards and microcontrollers but they have not been yet democratized on System on chip (SoC) such as those that can be found in smart phones, tablets and automotive systems. The main reason behind this is the complexity to inject a fault at the right place and at the right time to make these attacks effective on such devices. However for...
Active Constraint Learning (ACL) is continuously gaining popularity in the area of constrained clustering due to its ability to achieve performance gains via incorporating minimal feedback from a human annotator for selected instances. For constrained clustering algorithms, such instances are integrated in the form of Must-Link (ML) and Cannot-Link (CL) constraints. Existing iterative uncertainty...
In this paper, we propose the encoding and list decoding method of polar codes based on the four-dimensional Reed-Solomon (RS-4) kernel. In specific, an encoding table based method is employed to reduce the computational complexity of both encoder and decoder. In addition, a simplified method to update log-likelihood ratios (LLRs) which employs additions instead of exponential calculations is also...
This paper introduces a fast blind deconvolution strategy for image deblurring by modifying a recent natural image model, i.e., the total generalized variation (TGV), which aims at reconstructing an image with higher-order smoothness as well as sharp edges. But, when it turns to the blind issue, as demonstrated either empirically or theoretically by a few previous blind deblurring works, natural image...
In the hyperspectral image classification area, a few number of labeled samples is a bottleneck for the improvement of classification accuracy. In order to tackle this problem, multiple one-dimensional embedding interpolation (M1DEI) has been used for hyperspectral image classification and achieved promising results. Despite the success, the complexity of M1DEI prevents its practical application....
Human trafficking is among the most challenging law enforcement problems which demands persistent fight against from all over the globe. In this study, we leverage readily available data from the website “Backpage”- used for classified advertisement- to discern potential patterns of human trafficking activities which manifest online and identify most likely trafficking related advertisements. Due...
High performance filtering has been in ever increasing demand for a range of applications, especially for real-time image/video processing. Guided image filter is one of the widely used image filters. Among them, the gradient domain guided image filter for edge-preserving smoothing and for mitigating the halo-artifacts problem existed in the current guided image filters is reported recently. Due to...
We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS...
We introduce COCOTTE (COnstrained Complexity Optimization Through iTerative merging of Experts), an iterative algorithm for discovering discrete, meaningful parameterized skills and learning explicit models of them from a set of behaviour examples. We show that forward-parameterized skills can be seen as smooth components of a locally smooth function and, framing the problem as the constrained minimization...
Bilateral filtering is a commonly used technique in image processing. However, being nonlinear, it is computationally expensive. The situation gets worse while the filter radius grows up. Several works have been proposed to accelerate the computation. Nevertheless, most techniques are tailored for grayscale image bilateral filtering or confined to specific kernel functions. In this paper, we propose...
Cooperative localization capability is a highly desirable characteristic of wireless sensor networks. It has attracted considerable research attention in academia and industry. The sum-product algorithm over a wireless sensor network (SPAWN) is a powerful method to cooperatively estimate the positions of many sensors (agents) using knowledge of the absolute positions of a few sensors (anchors). Drawbacks...
This paper presents a novel reduced-rank approach for implementing Volterra filters with reduced complexity. Such an approach is based on the application of the singular value decomposition to a new form of coefficient matrix obtained by exploiting the representation based on diagonal coordinates of the Volterra kernels. The result is a parallel structure of extended Hammerstein models in which each...
Abstract-Variance computation is commonly used in many fields like in image processing to improve local contrasts. This article is not only about developing and placing an algorithm of variance computation for graphical processors, it will also introduce its optimisation in terms of precision and computing time in relation to architectural constraints of graphical processors. Our algorithm enables...
Maximum Likelihood Sequence Detectors (MLSD) have been largely used to mitigate the Chromatic Dispersion (CD) in Intensity Modulation/Direct Detection (IM/DD) optical communication systems. For practical applications, the high complexity of the receivers remains an important issue. In this paper, we analyze the design of MLSD-based receivers using the Orthogonal Volterra Kernel Model for IM/DD optical...
A O(N) solver is proposed for the acceleration of iterative solutions of the Method of Moments (MoM) matrix; the proposed strategy uses a local compression scheme based on sampling the field radiated by groups of basis functions. The local compression scheme plus the use of a nested structure to represent the low rank part of the MoM matrix grant the linear complexity demonstrated through numerical...
Quantitatively characterizing wind variation series in different environments is an important problem with significant engineering and industrial applications. We systematically carry out indoor and outdoor experiments separately to record two groups of wind speed time series. Using the Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) we first investigate the fluctuating behaviors from...
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