Displaying similar documents to “Directional representation of data in Linear Discriminant Analysis”

Analysis of correlation based dimension reduction methods

Yong Joon Shin, Cheong Hee Park (2011)

International Journal of Applied Mathematics and Computer Science

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Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised...

An algorithm for reducing the dimension and size of a sample for data exploration procedures

Piotr Kulczycki, Szymon Łukasik (2014)

International Journal of Applied Mathematics and Computer Science

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The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease...

Dissimilarites de type spherique et positionnement multidimensionnel normé

Farid Beninel (2010)

RAIRO - Operations Research

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Our concern here, is the characterization of dissimilarity indexes defined over finite sets, whose spatial representation is spherical. Consequently, we propose a methodology (Normed MultiDimensional Scaling) to determine the spherical euclidean representation of a set of items best accounting for the initial dissimilarity between items. This methodology has the advantage of being graphically readable on individual qualities of projection like the normed PCA, of which it constitutes...

On the optimality of the max-depth and max-rank classifiers for spherical data

Ondřej Vencálek, Houyem Demni, Amor Messaoud, Giovanni C. Porzio (2020)

Applications of Mathematics

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The main goal of supervised learning is to construct a function from labeled training data which assigns arbitrary new data points to one of the labels. Classification tasks may be solved by using some measures of data point centrality with respect to the labeled groups considered. Such a measure of centrality is called data depth. In this paper, we investigate conditions under which depth-based classifiers for directional data are optimal. We show that such classifiers are equivalent...

On the five-point theorems due to Lappan

Yan Xu (2011)

Annales Polonici Mathematici

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By using an extension of the spherical derivative introduced by Lappan, we obtain some results on normal functions and normal families, which extend Lappan's five-point theorems and Marty's criterion, and improve some previous results due to Li and Xie, and the author. Also, another proof of Lappan's theorem is given.

Protecting micro-data by micro-aggregation: the experience in Eurostat.

Daniel Defays (1997)

Qüestiió

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A natural strategy to protect the confidentiality of individual data is to aggregate them at the lowest possible level. Some studies realised in Eurostat on this topic will be presented: properties of classifications in clusters of fixed sizes, micro-aggregation as a generic method to protect the confidentiality of individual data, application to the Community Innovation Survey. The work performed in Eurostat will be put in line with other projects conducted at European level on the...

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Fokoue, Ernest (2014)

Serdica Journal of Computing

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Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of...

Warehousing and OLAP Analysis of Data About Unique Bulgarian Bells Складиране и OLAP анализ на данни за уникални български камбани

Trifonov, Tihomir, Georgieva-Trifonova, Tsvetanka (2012)

Union of Bulgarian Mathematicians

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Тихомир Трифонов, Цветанка Георгиева-Трифонова - В настоящата статия е представена системата bgBell/OLAP за складиране и онлайн аналитична обработка на данни за уникални български камбани. Реализираната система предоставя възможност за извеждане на обобщени справки и анализиране на различни характеристики на камбаните, за да се извлече предварително неизвестна и потенциално полезна информация. In this paper, the system bgBell/OLAP for warehousing and online analytical processing...

Data mining techniques using decision tree model in materialised projection and selection view.

Y. W. Teh (2004)

Mathware and Soft Computing

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With the availability of very large data storage today, redundant data structures are no longer a big issue. However, an intelligent way of managing materialised projection and selection views that can lead to fast access of data is the central issue dealt with in this paper. A set of implementation steps for the data warehouse administrators or decision makers to improve the response time of queries is also defined. The study concludes that both attributes and tuples, are important...