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Two-Phase Mapping for Projecting Massive Data Sets
(IEEE COMPUTER SOC, 2010)
Most multidimensional projection techniques rely on distance (dissimilarity) information between data instances to embed high-dimensional data into a visual space. When data are endowed with Cartesian coordinates, an extra ...
Radar Noise Reduction Based on Binary Integration
(IEEE, 2015)
Short range radars can provide robust information
about their surroundings under atmospheric disturbances,
such as dust, rain, and snow, conditions under which most
other sensing technologies fail. However, this information ...
How optimizing perplexity can affect the dimensionality reduction on word embeddings visualization?
(Springer, 2019-12-01)
Traditional word embeddings approaches, such as bag-of-words models, tackles the problem of text data representation by linking words in a document to a binary vector, marking their occurrence or not. Additionally, a term ...
Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis
(SAGE Publications Ltd, 2019)
© The Author(s) 2018.One of the main challenges that the industry faces when dealing with massive data for failure diagnosis is high dimensionality of such data. This can be tackled by dimensionality reduction method such ...
A note on Smoothed Functional Inverse Regression
(Statistica Sinica, 2007-12)
Estimation in the context of functional data analysis is almost always non-parametric, since the object to be estimated lives in an infinite dimensional space. That is the case for the functional linear model with a ...
Carbothermal Reduction of Iron Ore Applying Microwave Energy
(WILEY-BLACKWELLMALDEN, 2012)
This paper presents the results of a study on carbothermal reduction of iron ore made under the microwave field in equipment specially developed for this purpose. The equipment allows the control of radiated and reflected ...
Clustering biological data with SOMs: on topology preservation in non-linear dimensional reduction
(Pergamon-Elsevier Science Ltd, 2013-07)
Dimensional reduction is a widely used technique for exploratory analysis of large volume of data. In biological datasets, each object is described by a large number of variables (or dimensions) and it is crucial to perform ...
Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data
(WILEY-BLACKWELLHOBOKEN, 2012)
Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve ...