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Multiview canonical correlation analysis (MCCA) is an effective tool for analyzing the relationships among group- aligned multidimensional samples, which has been applied to the fields of pattern recognition and computer vision. In MCCA, its first-stage canonical variables are solved by a multivariate eigenvalue problem that can be computed by Horst method. However, how to use the algorithm for effectively...
Bioacoustics signals classification is an important instrument used in environmental monitoring as it gives the means to efficiently acquire information from the areas, which most of the time are unfeasible to approach. To address these challenges, bioacoustics signals classification systems should meet some requirements, such as low computational resources capabilities. In this paper, we propose...
Academic performance is one of the indicators to evaluate the achievement of college students, which plays an important role in individual development. However, it is worth mentioning that personality traits and thinking styles have an influence on academic performance. To further study the correlation among personality traits, thinking styles and academic performance, we carry out a series of experiments...
A time-frequency analysis based approach for the decomposition of bivariate signals is presented. In particular, the well-known problem of two components overlapping in the time-frequency plane while having non-linear instantaneous frequencies is considered. The bivariate form of data leads to a significant modification of the Wigner distribution cross-terms. Therefore, the eigenvalue decomposition...
A novel solution of the inverse Frobenius-Perron problem for constructing semi-Markov chaotic maps with prescribed statistical properties is presented. The proposed solution uses recursive Markov state disaggregation to construct an ergodic map with a piecewise constant invariant density function that approximates an arbitrary probability distribution over a compact interval. The solution is novel...
The comprehensive index system is a prerequisite and basis for ensuring the scientific performance of the product performance evaluation, so, the screening of the index is the necessary link in the construction of the index system. The paper regards the armored vehicle PHM system as a special product, aiming at the problem that the redundancy of the primary index set of the armored vehicle PHM system...
We addressed two areas of concern regarding the analysis of a financial time series with a correlation structure, coarse graining (or renormalization) and the extraction of leading and lagging structures. We introduce the complex Hilbert principal component analysis to solve these two problems, and apply them to the time series of 33 Tokyo Stock Exchange industry indices and Tokyo Stock Price Index...
In order to find a set of optimal discriminant vectors which can maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection, a new algorithm of orthogonal optimal discriminant vectors and a new algorithm of statistically uncorrelated optimal discriminant vectors for feature extraction were proposed. Compared with the original MMC feature extraction...
We present our recent work on the Weyl-Heisenberg ensemble and its statistical properties [4]. The WH ensemble is a class of determinantal point processes associated with the Schrödinger representation of the Heisenberg group. As a special example, WH ensembles include a multi-layer extension of the Ginibre ensemble modeling the distribution of electrons in higher Landau levels. We describe the hyperuniformity...
Information fusion aims to exploit truthful knowledge from various sources in a reliable and accurate way. Fusion of information can be conducted at three abstraction levels including feature level, score level and decision level. The feature fusion approaches have the advantages of preserving effective discriminative structure underlying various features. In this paper, we propose an effective feature...
Multiple view data with different feature representations have widely arisen in various practical applications. Due to the information diversity, fusing multiview features is very valuable for classification purpose. In this paper, we propose a new multifeature fusion method called fractional-order discriminative multiview correlation projection (FDMCP), which is based on fractional-order scatter...
The multi-homing technique in the dynamic spectrum access networks presents the key mechanism to improve the overall spectrum utilization and social surplus of the end-users willing to communicate. In general, multi-homing allows the enduser to switch dynamically among the operators and together with other bunch of protocols are being developed in the IETF. They aim for better utilizing the network...
When performing a separation of test results, coping with enormous high-dimensional data sets is necessary but problematic. The input of high-dimensional data, in which not a few elements are irrelevant or less relevant than others, usually lead to inadequate results. It is therefore useful to consult methods, which classify the individual dimensions of the data volumes according to their relevance...
The article considers the pre-processing voice signals for voice recognition systems based on the use of artificial neural networks. Based segmentation preprocessing is put in the speech signal according to a phonetic transcription of language, in order to reduce the amount of data supplied to the input of the neural network, which considerably improves its input data sensitivity. Application of numerical...
In this paper, we put forward an improved Android malware detection method. When the Android malware is in the execution, the method can capture various features from Android malware and then apply machine learning technology to classify the android applications into different categories. Also the method we proposed is improved combining feature correlation and Bayes classification model. Experiment...
This work puts forward the problem of blind mixing matrix identification in the case of linearly mixed signals of cyclostationay sources whose cyclic frequencies are unknown and different. The identification is achieved using a semi-analytical solution. It takes advantage of the Eigenvalue Decomposition (EVD) of a set of algebraically particular matrices resulted from the application of the cyclic...
A massive explosion of various types of data has been triggered in the “Big Data” era. In big data systems, machine learning plays an important role due to its effectiveness in discovering hidden information and valuable knowledge. Data privacy, however, becomes an unavoidable concern since big data usually involve multiple organizations, e.g., different healthcare systems and hospitals, who are not...
This paper deals with simple and effective residual carrier frequency offsets (CFO) estimation approaches for interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. It has been shown that the propagator method (PM) has low computational load and is efficient in noise free environments. However, the PM is not robust to noise. With the forward-backward (FB) correlation matrix...
In commercial banks, data centers often integrates different data sources, which represent complex and independent business systems. Due to the inherent data variability and measurement or execution errors, there may exist some abnormal customer records (data). Existing automatic abnormal customer detection methods are outlier detection which focuses on the differences between customers, and it ignores...
Recently, we systematically investigated short-term memory of an echo state network fed with a scalar random input, using computational simulations. We studied the effect of proper reservoir initialization and its subsequent orthogonalization, using two similar gradient descent iterative procedures. It was shown that the measure defined by Jaeger as memory capacity (MC) approached its theoretical...
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