Search results for "DIMENSION"

A Novel Self-organizing Neural Technique for Wind Speed Mapping

2009

Systems with high nonlinearities are, in general, very difficult to model. This is particularly true in geostatistics, where the problem of the estimation of a regionalized variable (RV) given only a small amount of measurement stations and a complex terrain surface is very challenging. This paper introduces a novel strategy, which couples the Curvilinear Component Analysis (CCA) and the Generalized Mapping Regressor (GMR). CCA, which is a nonlinear projector of a data manifold, is here used in order to find the intrinsic dimension of the data manifold, just giving an insight on the nonlinearities of the problem. This analysis drives the pre-processing of the data set used for the training …

Polar Classification of Nominal Data

2013

Many modern systems record various types of parameter values. Numerical values are relatively convenient for data analysis tools because there are many methods to measure distances and similarities between them. The application of dimensionality reduction techniques for data sets with such values is also a well known practice. Nominal (i.e., categorical) values, on the other hand, encompass some problems for current methods. Most of all, there is no meaningful distance between possible nominal values, which are either equal or unequal to each other. Since many dimensionality reduction methods rely on preserving some form of similarity or distance measure, their application to such data sets…

Online Density Estimation of Heterogeneous Data Streams in Higher Dimensions

2016

The joint density of a data stream is suitable for performing data mining tasks without having access to the original data. However, the methods proposed so far only target a small to medium number of variables, since their estimates rely on representing all the interdependencies between the variables of the data. High-dimensional data streams, which are becoming more and more frequent due to increasing numbers of interconnected devices, are, therefore, pushing these methods to their limits. To mitigate these limitations, we present an approach that projects the original data stream into a vector space and uses a set of representatives to provide an estimate. Due to the structure of the est…

Gathering formalized information requirements of a data warehouse

2017

Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis

2006

Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection w…

Feature extraction for classification in knowledge discovery systems

2003

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of "the curse of dimensionality". We consider three different eigenvector-based feature extraction approaches for classification. The summary of obtained results concerning the accuracy of classification schemes is presented and the issue of search for the most appropriate feature extraction method for a given data set is considered. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the d…

Decorin transfection induces proteomic and phenotypic modulation in breast cancer cells 8701-BC

2008

Decorin is a prototype member of the small leucine-rich proteoglycan family widely distributed in the extracellular matrices of many connective tissues, where it has been shown to play multiple important roles in the matrix assembly process, as well as in some cellular activities. A major interest for decorin function concerns its role in tumorigenesis, as growth-inhibitor of different neoplastic cells, and potential antimetastatic agent. The aim of our research was to investigate wide-ranged effects of transgenic decorin on breast cancer cells. To this purpose we utilized the well-characterized 8701-BC cell line, isolated from a ductal infiltrating carcinoma of the breast, and two derived …

Feature selection for KNN classifier to improve accurate detection of subthalamic nucleus during deep brain stimulation surgery in Parkinson’s patien…

2017

The tremor and dystonia associated with Parkinson’s disease can be treated with deep brain stimulation (DBS) implanted into the subthalamic nucleus (STN). The accurate STN detection is a complex neurosurgeon task during a DBS surgery since a proper fixing of stimulating electrodes will impact on the patient’s future life. The brain electrical signals obtained with Micro Electrodes Register (MER) are acquired at different depths of the brain during DBS surgery to detect STN. In our previous work, we found good accuracy performance to improve the localization of STN using K-Nearest Neighbours (KNN) supervised learning algorithm. However, for real-time classification, it is essential to reduce…

A ML Estimator of the Correlation Dimension for Left-hand Truncated Data Samples

2002

— A maximum-likelihood (ML) estimator of the correlation dimension d 2 of fractal sets of points not affected by the left-hand truncation of their inter-distances is defined. Such truncation might produce significant biases of the ML estimates of d 2 when the observed scale range of the phenomenon is very narrow, as often occurs in seismological studies. A second very simple algorithm based on the determination of the first two moments of the inter-distances distribution (SOM) is also proposed, itself not biased by the left-hand truncation effect. The asymptotic variance of the ML estimates is given. Statistical tests carried out on data samples with different sizes extracted from populatio…

Effects of minute misregistrations of prefabricated markers for image-guided dental implant surgery: an analytical evaluation

2012

Objectives The goal of the present study was to develop a theoretical analysis of errors in implant position, which can occur owing to minute registration errors of a reference marker in a cone beam computed tomography volume when inserting an implant with a surgical stent. Material and methods A virtual dental-arch model was created using anatomic data derived from the literature. Basic trigonometry was used to compute effects of defined minute registration errors of only voxel size. The errors occurring at the implant's neck and apex both in horizontal as in vertical direction were computed for mean ±95%-confidence intervals of jaw width and length and typical implant lengths (8, 10 and 1…