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A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the...
The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures but are still computationally expensive. We propose a novel graph kernel based on the structural characteristics of graphs. The key is to represent node labels as binary arrays and characterize each node using logical operations...
Mean shift spectral clustering (MSSC) brings us an alternative for image segmentation. However, owing to being based on the classical Parzen window estimator (PW) and employing the full data sample for density estimation, the usefulness of MSSC is weakened. In this paper, the improved mean shift spectral clustering (IMSSC) algorithm is proposed by replacing PW with the reduced set density estimator...
This work presents a new blind multiuser equalization strategy for Time Reversal Space Time Block Coding (TRSTBC) signals transmitted over a dispersive MIMO channel. The adaptation is based on forcing the probability density function (PDF) of the equalizer output to match the desired PDF of corresponding source symbols. In the proposed approach, the PDFs are estimated by means of the Parzen window...
In this paper, we propose an efficient method for resolving the optimal discriminant vectors of generalized discriminant analysis (GDA) and point out the drawback of high computational complexity in the traditional class-incremental GDA [W. Zheng, "Class-Incremental Generalized Discriminant Analysis", Neural Computation 18, 979-1006 (2006)]. Because there is no need to compute the mean of...
State-of-the-art integral-equation based computational electromagnetic methods rely on techniques that can perform a matrix-vector multiplication in O(NlogN) operations, with N being the matrix size. In this work, a fast integral-equation-based solver was developed for solving large-scale electrodynamic problems. Both memory consumption and time complexity were shown to be O(N). The superior performance...
Kernel Fisher discriminant analysis (KFDA) has been widely used in fault diagnosis. In this paper, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KFDA when the number of samples becomes large. Experimental results show the effectiveness of our method.
The FreeBSD, GNU/Linux, Solaris, and Windows operating systems have kernels that provide comparable facilities. Interestingly, their code bases share almost no common parts, while their development processes vary dramatically. We analyze the source code of the four systems by collecting metrics in the areas of file organization, code structure, code style, the use of the C preprocessor, and data organization...
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