We present a novel algorithm for point pattern matching by means of spectra of directed graphs. Given a feature point-set, we construct a weighted directed graph and skew-symmetric matrix associated with the graph. By...
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We present a novel algorithm for point pattern matching by means of spectra of directed graphs. Given a feature point-set, we construct a weighted directed graph and skew-symmetric matrix associated with the graph. By using spectral decomposition of the matrix, we give a spectral representation of the feature points with half of the eigenvectors. We theoretically analyze that our method can well deal with the matching problem under affine transformation. The expreiments applied to synthetic data and real-world images show the effectiveness of our method.
To deal with the insufficiency problem of Laplacian eigenmap (LE) method and Maximum margin criterion (MMC) method in feature extraction, a new dimensionality reduction method called Laplacian eigenmap based on Improv...
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To deal with the insufficiency problem of Laplacian eigenmap (LE) method and Maximum margin criterion (MMC) method in feature extraction, a new dimensionality reduction method called Laplacian eigenmap based on Improved maximum margin criterion (LE/IMMC) is proposed with applications in gene expression data classification. The LE/IMMC intends to constrain similar data points as close to each other as possible and maximize the margin regions between different pattern classes simultaneously. The proposed LE/IMMC by introducing IMMC into the cost function of LE retains the characteristic of local neighborhood relationship of LE. Meanwhile, it emphasizes the discriminative information by incorporating IMMC, which can maximize the between-class scatter and minimize the within-class scatter. Gene expression data classification experiments on four public datasets demonstrate our method is effective for feature extraction,
In this paper, we propose two new subband adaptive filtering (SAF) algorithms based on the cost function of the logistic distance metric cost function and use the proportionate proximal gradient algorithm to exploit t...
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The multi-section coupled transmission line is equivalent to the cascade of multi-section ladder impedance filters, which provides a basis for the research of broadband directional couplers and meets the needs of micr...
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Community detection is of great importance to find hidden information in complex networks. For this problem, local expansion algorithms are becoming popular due to the low time complexity. However, most of them depend...
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Aiming at the inability to reconstruct the frequency and the pattern of the traditional monopole antenna, an ultra-wideband monopole antenna with frequency reconstruction and pattern reconstruction is proposed. The an...
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During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable ***,due to the stochastic sear...
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During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable ***,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision *** tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal *** proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search *** importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by *** existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real ***,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.
Organs-at-risk segmentation is critical for ensuring the safety and precision of radiotherapy and surgical procedures. However, existing methods for organs-at-risk image segmentation often suffer from uncertainties an...
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In addressing the issue of localizing near-field source signals in the presence of impulse noise, this manuscript introduces a novel de-impact function designed for preprocessing received signals to alleviate the inte...
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To achieve high precision direction-of-arrival estimation of MIMO arrays in highly coupled environments, based on the difference and cooperative array principles, the uniform array fitted with 5 base layer array and t...
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