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A Dimensionality Reduction Technique
, 1980
"... A dimensionality reduction technique for enhancing information context. ..."
Outlier Preservation by Dimensionality Reduction Techniques
, 2013
"... Sensors are increasingly part of our daily lives: motion detection, light-ing control, and energy consumption all rely on sensors. Combining this information into, for instance, simple and comprehensive graphs can be quite challenging. Dimensionality reduction is often used to address this problem, ..."
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unnoticed.In this paper we show that dimensionality re-duction can indeed have a large impact on outliers. To that end we apply three dimensionality reduction techniques to three real-world data sets, and inspect how well they preserve outliers. We use several performance measures to show how well
An Empirical Comparison of Dimensionality Reduction Techniques for Pattern Classification
"... . To some extent or other all classifiers are subject to the curse of dimensionality. Consequently, pattern classification is often preceded with finding a reduced dimensional representation of the patterns. In this paper we empirically compare the performance of unsupervised and supervised dimensio ..."
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dimensionality reduction techniques. The data set we consider is obtained by segmenting cells in cytological preparations and extracting 9 features from each of the cells. We evaluate the performance of 4 dimensionality reduction techniques (2 unsupervised) and (2 supervised) with and without noise
NONLINEAR DIMENSIONALITY REDUCTION TECHNIQUES AND THEIR APPLICATIONS
"... Dimensionality reduction is the search for a small set of variables to describe a large set of observed dimensions. Some benefits of dimensionality reduction include data visualization, compact representation, and decreased ..."
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Dimensionality reduction is the search for a small set of variables to describe a large set of observed dimensions. Some benefits of dimensionality reduction include data visualization, compact representation, and decreased
Comparative Analysis of Dimensionality Reduction Techniques
"... ABSTRACT: Datasets are most important for performing all the type of data mining tasks. Every dataset has many numbers of attributes and instances. Dimensionality reduction (DR) is one of the preprocessing steps which is used to reduce the dimensions (attributes or features) without losing the data ..."
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by eliminating features with little or no predictive information. Feature extraction techniques are more adequate than the feature selection. Reduction is done to the larger dataset to decrease the curse of dimensionality. The main objective of this paper is to provide a systematic comparative analysis
A Comparative Analysis of Dimensionality Reduction Techniques
, 2006
"... How can we represent a data residing in high dimensional space onto a low dimensional space without the loss of important information? In image processing, pattern recognition, machine learning and in many other fields like social science, statistics, signal processing etc, the measured data set oft ..."
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representation of the high dimensional data. We discuss a range of dimensionality reduction techniques on the basis of their underlying concept. The methods of PCA, FA, ICA, MDS, ISOMAP, LLE and GA based methods have been discussed in this paper. We also comparatively analyze each of them based on their merit
‐ 1 ‐ A survey of dimensionality reduction techniques
"... Abstract—Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and theref ..."
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therefore the statistical methods face challenging tasks when dealing with such high‐dimensional data. However, much of the data is highly redundant and can be efficiently brought down to a much smaller number of variables without a significant loss of information. The mathematical procedures making possible this reduction are called dimensionality reduction
Non-Linear Dimensionality Reduction Techniques for Classification and Visualization
- in Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2002
"... In this paper we address the issue of using local embeddings for data visualization in two and three dimensions, and for classification. We advocate their use on the basis that they provide an efficient mapping procedure from the original dimension of the data, to a lower intrinsic dimension. We dep ..."
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Cited by 37 (1 self)
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depict how they can accurately capture the user's perception of similarity in high-dimensional data for visualization purposes. Moreover, we exploit the low-dimensional mapping provided by these embeddings, to develop new classification techniques, and we show experimentally that the classification
Map Building without Localization by Dimensionality Reduction Techniques
"... This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is interpreted as a problem of reconstructing the 2-D coordinates of objects so that they maximally preserve the local prox ..."
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Cited by 5 (0 self)
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This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is interpreted as a problem of reconstructing the 2-D coordinates of objects so that they maximally preserve the local
Results 1 - 10
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55,744
