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In this paper we describe a method for nonlinear class-specific discriminant learning that is based on Cholesky Decomposition. We show that the optimization problem solved in Class-Specific Kernel Discriminant Analysis is equivalent to that of Low-Rank Kernel Regression using training data independent target vectors. This connection allows us to devise a new Class-Specific Kernel Discriminant Analysis...
The major contribution of this work is on the reduced complexity of controller design for a class of distributed parameter systems. An optimal full state controller is first designed and the full state feedback operator is assumed to admit a kernel representation (integral representation). With the aid of the feedback kernel, the spatial domain of definition of the distributed parameter system is...
Large datasets in astronomy and geoscience often require clustering and visualizations of phenomena at different densities and scales in order to generate scientific insight. We examine the problem of maximizing clustering throughput for concurrent dataset clustering in spatial dimensions. We introduce a novel hybrid approach that uses GPUs in conjunction with multicore CPUs for algorithmic throughput...
For many problems in machine learning fields, the data are nonlinearly distributed. One popular way to tackle this kind of data is training a local kernel machine or a mixture of several locally linear models. However, both of these approaches heavily relies on local information, such as neighbor relations of each data sample, to capture potential data distribution. In this paper, we show the non-local...
This paper deals with the recently introduced class of Non-Surjective Finite Alphabet Iterative Decoders (NS-FAIDs). First, optimization results for an extended class of regular NS-FAIDs are presented. They reveal different possible trade-offs between decoding performance and hardware implementation efficiency. To validate the promises of optimized NS-FAIDs in terms of hardware implementation benefits,...
Autonomous robots have significant potential for reconnaissance and environmental monitoring applications. Ground robots, in particular, are performing reconnaissance missions in places that are too hazardous for humans. However, these robots are constrained by energy limitations that are impacted by uncertain environments and harsh terrains. The purpose of this work is to develop methods for improving...
A kernel-based nonparametric approach to identification of linear systems in the presence of bounded noise affecting both input and output measurements is proposed in this paper. The problem to be solved is firstly formulated in terms of robust optimization. The solution to such a problem is then obtained by proving that the originally formulated robust optimization problem is equivalent to a standard...
In order to improve the accuracy of support vector machine (SVM) classification of wetland remote sensing images, the selection of kernel function parameters in support vector machines becomes an effective approach. In this paper, Particle Swarm Optimization and Genetic Algorithms (PSO-GA) co-evolutionary algorithm are used to optimize the SVM parameters. Because of the complementarity of evolutionary...
We explore the use of synthetic benchmarks for the training phase of machine-learning-based automatic performance tuning. We focus on the problem of predicting if the use of local memory on a GPU is beneficial for caching a single target array in a GPU kernel. We show that the use of only 13 real benchmarks leads to poor prediction accuracy (about to 58%) of the 13 leave-one-out models trained using...
We present a novel strategy for automatic performance tuning of GPU computational kernels. The strategy combines heuristic search with regression trees to prune the optimization space. It samples configurations in the space and uses these samples to build a regression tree. It then focuses the search on the leaf region of the tree with the best mean sample performance. Additional configurations are...
In the process of large-scale chemical engineering, more useful industrial process information can be obtained by increasing measuring variables, defined as system features. However, the increase in amount of features will lead to the high computation cost and reduce the efficiency of the process monitoring system. To solve this issue, those features that are redundant or bring an incorrect result...
Today the technology advancement in communication technology permits a malware author to introduce code obfuscation technique, for example, Application Programming Interface (API) hook, to make detecting the footprints of their code more difficult. A signature-based model such as Antivirus software is not effective against such attacks. In this paper, an API graph-based model is proposed with the...
This paper mainly focuses on the optimization of rotor structures of permanent magnet (PM) motor. It combines global optimization method with the finite-element computation and the response surface model for solving the single-objective optimization for the motor structure. By varying the design parameters of a general pattern of rotor topologies, it is capable of simulating for different types of...
It is well known that the TLB performance impacts the memory system performance, which is critical for overall system performance. Similar to multi-level caches, multilevel TLBs have become an important leverage for boosting data access performance. Applications have increasingly large working sets. Servers targeting such applications have thus been built with ever larger main memory capacities, but...
An interesting class of irregular algorithms is tree traversal algorithms, which repeatedly traverse various trees to perform efficient computations. Tree traversal algorithms form the algorithmic kernels in an important set of applications in scientific computing, computer graphics, bioinformatics, and data mining, etc. There has been increasing interest in understanding tree traversal algorithms,...
Non-local means (NLM) filtering of fMRI can reduce noise while preserving spatial structure. We have developed a variant called temporal-NLM (tNLM) which uses similarity in time-series between voxels as the basis for computing the weights in the filter. Using tNLM, dynamic fMRI data can be denoised while spatial boundaries between functionally distinct areas in the brain tend to be preserved. The...
The weights vector in the output linear computation of Radial Basis Function Neural Network (RBFNN) plays an essential role in achieving an optimal classification decision. RBFNN defects occur when classification confusion shows both misclassification and non-existent classification; mainly due to poor weight selection strategy. In an attempt to improve upon these problems, we implemented a learning...
Sparsity in the weights of deep convolutional networks presents a tremendous opportunity to reduce computational requirements. In order to optimize flow of traffic systems, any viable solution must be able to operate at real-time. Existing computation frameworks do not yet realize the full potential speedup afforded by sparse neural networks. Meanwhile, the power consumption for a GPU is too great...
Finding the k nearest neighbors of a query point or a set of query points (KNN) is a fundamental problem in many application domains. It is expensive to do. Prior efforts in improving its speed have followed two directions with conflicting considerations: One tries to minimize the redundant distance computations but often introduces irregularities into computations, the other tries to exploit the...
Co-saliency detection aims at finding the common salient objects in multiple images. In this paper, we introduce a new co-saliency detection model, which includes two main parts: co-salient seed selection using the inter-object recurrence cues from multiple images and saliency label propagation using partially absorbing random walk. With the guidance of co-salient seeds, salient objects are individually...
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