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The one-class classification problemis often addressed by solving a constrained quadratic optimization problem, in the same spirit as support vector machines. In this paper, we derive a novel one-class classification approach, by investigating an original sparsification criterion. This criterion, known as the coherence criterion, is based on a fundamental quantity that describes the behavior of dictionaries...
Performance is of utmost importance for linear algebra libraries since they usually are the core of numerical and simulation packages and use most of the available compute time and resources. However, especially in large scale simulation frameworks the readability and ease of use of mathematical expressions is essential for a continuous maintenance, modification, and extension of the software framework...
This paper proposes an optimal orthogonal overcomplete kernel design for sparse representation such that the sum of the L1 norms of a set of transformed vectors is minimized. When there is only one training vector in the set, both the optimal transformed vector and the optimal orthogonal kernel are derived analytically. When there is more than one training vector in the sets, this optimization problem...
This paper proposes to use a set of discrete fractional Fourier transform (DFrFT) matrices with different rotational angles to construct an overcomplete kernel for sparse representations of signals. The design of the rotational angles is formulated as an optimization problem. To solve the problem, it is shown that this design problem is equivalent to an optimal sampling problem. Furthermore, the optimal...
In this paper, we present a novel framework for sparse kernel learning in a finite space called the Empirical Kernel Feature Space (EKFS). The EKFS can be explicitly built by using any positive definite kernel including Gaussian RBF kernel via an empirical kernel map. In order to turn the empirical kernel map into a feature map associated with a kernel, EKFS is endowed with the dot product of a map...
Raptor code, a member of the fountain code family, is a significant theoretical improvement over the Luby transform code (LT code) for forward error correction (FEC) transmission. Graphics processing units (GPUs) have become a common place in the consumer market and are finding their way beyond graphics processing into general purpose computing. This paper investigates the suitability of GPU for Raptor...
Multi-sphere Support Vector Data Description (MS-SVDD) has been proposed in our previous work. MS-SVDD aims to build a set of spherically shaped boundaries that provide a better data description to the normal dataset and an iterative learning algorithm that determines the set of spherically shaped boundaries. MS-SVDD could improve classification rate for one-class classification problems comparing...
Limiting factors of fast and effective classifiers for large sets of images are their dependence on the number of images analyzed and the dimensionality of the image representation. Considering the growing number of images as a given, we aim to reduce the image feature dimensionality in this paper. We propose reduced linear kernels that use only a portion of the dimensions to reconstruct a linear...
Spectral clustering makes use of spectral-graph structure of an affinity matrix to partition data into disjoint meaningful groups. Because of its elegance, efficiency and good performance, spectral clustering has become one of the most popular clustering methods. Traditional spectral clustering assumes a single affinity matrix. However, in many applications, there could be multiple potentially useful...
A novel method is proposed for matching articulated objects in cluttered videos. The method needs only a single exemplar image of the target object. Instead of using a small set of large parts to represent an articulated object, the proposed model uses hundreds of small units to represent walks along paths of pixels between key points on an articulated object. Matching directly on dense pixels is...
Sparse matrix vector multiplication, SpMV, is often a performance bottleneck in iterative solvers. Recently, Graphics Processing Units, GPUs, have been deployed to enhance the performance of this operation. We present a blocked version of the Transposed Jagged Diagonal storage format which is tailored for GPUs, BTJAD. We develop a highly optimized SpMV kernel that takes advantage of the properties...
In this paper, we propose a rank minimization method to fuse the predicted confidence scores of multiple models, each of which is obtained based on a certain kind of feature. Specifically, we convert each confidence score vector obtained from one model into a pairwise relationship matrix, in which each entry characterizes the comparative relationship of scores of two test samples. Our hypothesis is...
Recent years have witnessed the growing popularity of hashing in large-scale vision problems. It has been shown that the hashing quality could be boosted by leveraging supervised information into hash function learning. However, the existing supervised methods either lack adequate performance or often incur cumbersome model training. In this paper, we propose a novel kernel-based supervised hashing...
Mean shift, like other gradient ascent optimization methods, is susceptible to local maxima, and hence often fails to find the desired global maximum. For this reason, mean shift segmentation algorithm based on bacterial colony chemotaxis (BCC) is proposed in this paper. The mean shift vector is firstly optimized using BCC algorithm. Then, the optimal mean shift vector is updated using mean shift...
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...
The geometric interpretation of Nonnegative Matrix Factorisation (NMF) as the problem of determining a convex cone that “well describes” the data under analysis has been key for addressing a major shortcoming of the “mainstream” NMF algorithms, that is the non-identifiability of the factorisation. On the basis of such geometric motivations, this paper proposes a novel algorithm that makes use of single-class...
Clustering often benefits from side information. In this paper, we consider the problem of multi-way constrained spectral clustering with pairwise constraints which encode whether two nodes belong to the same cluster or not. Due to the nontransitive property of cannot-link constraints, it is hard to incorporate cannot-link constraints into the framework. We settle this difficulty by restricting the...
Motivated by the fact that data of each cluster are often well captured by distinct features, we propose a clustering approach called multiple kernel self-organizing map (MK-SOM) that integrates multiple kernel learning into the learning procedure of SOM, and carries out cluster-dependent feature selection simultaneously. MK-SOM is developed to reveal the intrinsic relation between features and clusters,...
Hierarchical classification, decomposing the multi-class classification problem into binary ones hierarchically, is efficient when the class quantity getting large. Nowadays, the variety of features to describe data becomes huge and meanwhile the form of these features is diverse, which both make the task of feature fusion crucial for classification. In this paper, an adaptive kernel learning method,...
A useful tool for the efficient management of the electric power grid is the accurate, ahead-of-time prediction-of-load demand. A novel methodology for very-short-term load forecasting is introduced in this paper, and its performance is tested on a set of historical, demand-side, 5-min data. The approach employs an ensemble of kernel-based Gaussian processes (GPs) whose predictions constitute the...
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