Nonlinear Dimensionality Reduction Of
Large Datasets For Data Exploration
Author(s)
V. Tomenko & V. Popov
Abstract
Dimensionality reduction techniques are outlined; their strengths and limitations
are discussed. The novel dimensionality reduction method is presented, which is
a combination of input space approximation, nonlinear dimensionality reduction
and function approximation techniques. The method is especially useful for large
scale real-world datasets, where existing methods fail to succeed because of
extreme computational expenses. The method can be used in exploratory data
analysis and aims to create low dimensional data representation for better data
structure understanding and for cluster analysis. The comparison of
dimensionality reduction techniques is performed in order to justify the
applicability of the proposed method.
Keywords: dimensionality reduction, self-organizing neural network, data
exploration, function approximation.
1 Introduction
Advances in data collection and storage capabilities during the past decades have
led to data overload in most sciences. In such diverse domains as medicine,
biology, economics, astronomy and engineering, researchers have collected huge
datasets with larger and larger number of observations and of high
dimensionality. The dimension of the data is determined by the number of
variables that are measured on each observation. Such datasets, in contrast with
smaller, more traditional ones that have been studied extensively in the past,
present new challenges in data analysis, yet they must be processed and
understood in order to extend our knowledge in different domains.
Traditional statistical tools fail to meet the requirements of high-dimensional
data processing because of the increase in the number of variables associated
Keywords
dimensionality reduction, self-organizing neural network, data
exploration, function approximation.
Related Book
Other papers in this volume
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