A heuristic algorithm for pattern identification in large multivariate analysis of geophysical data sets

Joao Eduardo da Silva Pereira; Adelir Jose Strieder; Janete Pereira Amador; Jose Luiz Silverio da Silva; Leonidas Luiz Volcato Descovi Filho

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A heuristic algorithm for pattern identification in large multivariate analysis of geophysical data sets

机译：地球物理数据集大型多元分析中的模式识别启发式算法

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This paper aims to present a heuristic algorithm with factor analysis and a local search optimization system for pattern identification problems as applied to large and multivariate aero-geophysical data. The algorithm was developed in MATLAB code using both multivariate and univariate methodologies. Two main analysis steps are detailed in the MATLAB code: the first deals with multivariate factor analysis to reduce the problem of dimension, and to orient the variables in an independent and orthogonal structure; and the second with the application of a novel local research optimization system based on univariate structure. The process of local search is simple and consistent because it solves a multivariate problem by summing up univariate and independent problems. Thus, it can reduce computational time and render the efficiency of estimates independent of the data bank. The aero-geophysical data include the results of the magnetometric and gammaspectrometric (TC, K, Th, and U channels) surveys for the Santa Maria region (RS, Brazil). After the classification, when the observations are superimposed on the regional map, one can see that data belonging to the same subspace appear closer to each other revealing some physical law governing area pattern distribution. The analysis of variance for the original variables as functions of the subspaces obtained results in different mean behaviors for all the variables. This result shows that the use of factor transformation captures the discriminative capacity of the original variables. The proposed algorithm for multivariate factor analysis and the local search system open up new challenges in aero-geophysical data handling and processing techniques.

机译：本文旨在提出一种具有因子分析的启发式算法和一种用于模式识别问题的局部搜索优化系统，该系统应用于大型和多变量航空地球物理数据。该算法是使用多变量和单变量方法在MATLAB代码中开发的。 MATLAB代码中详细说明了两个主要分析步骤：第一个步骤用于进行多元因素分析，以减少尺寸问题，并使变量以独立且正交的结构定向。第二种是基于单变量结构的新型本地研究优化系统的应用。本地搜索的过程简单且一致，因为它通过汇总单变量和独立问题来解决多变量问题。因此，它可以减少计算时间，并使估算效率独立于数据库。航空地球物理数据包括圣玛丽亚地区（巴西，RS）的磁力和伽马能谱（TC，K，Th和U通道）调查的结果。分类后，将观测值叠加在区域地图上时，可以看到属于同一子空间的数据看起来彼此更接近，这揭示了一些控制区域模式分布的物理定律。对原始变量作为子空间函数的方差分析得出所有变量的平均行为不同。该结果表明，因子转换的使用捕获了原始变量的判别能力。提出的用于多元因素分析的算法和本地搜索系统为航空地球物理数据处理和处理技术提出了新的挑战。

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来源
《Computers & geosciences》 |2010年第1期|83-90|共8页
作者
Joao Eduardo da Silva Pereira; Adelir Jose Strieder; Janete Pereira Amador; Jose Luiz Silverio da Silva; Leonidas Luiz Volcato Descovi Filho;
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作者单位

Department of Statistics at the Federal University of Santa Maria. Santa Maria Avenida Roraima, no 1000 Cidade Universitaria Bairro Camobi, RS, Brazil;

Department of Mining Engineering at the Federal University of Rio Grande do Sul. Porto Alegre, Av. Paulo Gama, 110, RS, Brazil;

Department of Statistics at the Federal University of Santa Maria. Santa Maria Avenida Roraima, no 1000 Cidade Universitaria Bairro Camobi, RS, Brazil;

Department of Geosciences at the Federal University of Santa Maria. Santa Maria Avenida Roraima,1000 Cidade Universitaria Bairro Camobi, RS, Brazil;

Department of Geosciences at the Federal University of Santa Maria. Santa Maria Avenida Roraima,1000 Cidade Universitaria Bairro Camobi, RS, Brazil;

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正文语种 eng
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关键词
factor analysis; local search system; patterns identification; aero-geophysical data; MATLAB program;

机译：因子分析;本地搜索系统;模式识别;航空地球物理数据;MATLAB程序;

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7. Figure 4: (A) One conserved sequence, which occurs 79 times in 46,264 binding site peaks from the ChIP-seq data-set. The mutation profile of this conserved sequence is illustrated, where ’_ ’ indicates this base is unchanged; DEL indicates this base is lost; INS X indicates a new base X is inserted in front of this base. (B) Several repeated elements patterns are listed. (C) In the first column, the top five DNA motifs, mined by meme-chip tools (Machanick Bailey, 2011) are illustrated. The resemblant conserved sequences, found by the CFSP algorithm are listed in the second column. In the third column, the position-specific scoring matrices, which are transformed from mutational information are listed. The similarity between meme motif and resemblant conserved sequence with PSSM format was calculated via a stamp motif comparison tool (Mahony Benos, 2007). The E-values for the similarity of those pairs is displayed in the fourth column. (D) One motif is selected in each group clustered by gkmsvm descriptors, and the corresponding motif found by the CFSP algorithm is listed below. (E) There are additional datasets (File No: ENCFF100GRL, ENCFF616IRT, ENCFF870CER, Target: SREBF1) collected from https://www.encodeproject.org. The top two motifs are selected in each file using meme tools, and the corresponding motifs found by our algorithm are listed below. [O] . -1

机译：图4：（a）一种保守序列，其发生在芯片-SEQ数据集中的46,264个结合位点峰值中的79倍。说明了这种保守序列的突变分布，其中'_'表示该碱度不变; del表示此基础丢失; INS X表示新的基础X插入此基础前面。（b）列出了几种重复的元素模式。（c）在第一栏中，示出了由MEME芯片工具（Machanick＆Bailey，2011）开采的前五个DNA主题。由CFSP算法发现的相应保守序列列于第二列中。在第三列中，列出了从突变信息转换的特定位置的评分矩阵。 MEME主题与PSSM格式的相似性与PSSM格式之间的相似性通过邮票图章比较工具（Mahony＆Benos，2007）计算。这些对相似性的电子值显示在第四列中。（d）在由GKMSVM描述符聚集的每个组中选择了一个图案，下面列出了CFSP算法的相应主题。（e）从https://www.encodeproject.org收集的，有附加数据集（文件no：cernff100grl，cenf616irl，conf8.20cer，target：srebf1）。使用MEME工具在每个文件中选择前两个图案，并且我们的算法发现的相应主题如下所示。

A heuristic algorithm for pattern identification in large multivariate analysis of geophysical data sets

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