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Explaining dimensionality reduction results using Shapley values
(2021-09-15)
Dimensionality reduction (DR) techniques have been consistently supporting high-dimensional data analysis in various applications. Besides the patterns uncovered by these techniques, the interpretation of DR results based ...
Dimensionality reduction for the algorithm recommendation problem
(2018-12-13)
Given the increase in data generation, as many algorithms have become available in recent years, the algorithm recommendation problem has attracted increasing attention in Machine Learning. This problem has been addressed ...
Dimensionality reduction for visualization of normal and pathological speech data
(Elsevier, 2009-07)
For an adequate analysis of pathological speech signals, a sizeable number of parameters is required, such as those related to jitter, shimmer and noise content. Often this kind of high-dimensional signal representation ...
Contrastive analysis for scatterplot-based representations of dimensionality reduction
(2021-01-01)
Cluster interpretation after dimensionality reduction (DR) is a ubiquitous part of exploring multidimensional datasets. DR results are frequently represented by scatterplots, where spatial proximity encodes similarity among ...
A restricted boltzmann machine-based approach for robust dimensionality reduction
(2018-01-31)
Data dimensionality is an important issue to be adressed by pattern recognition systems. Despite of storage and processing, working with high-dimensional feature vectors also requires complex optimization methods. A proper ...
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 ...
Visual analysis of dimensionality reduction quality for parameterized projections
(Pergamon-Elsevier ScienceOxford, 2014-06)
In recent years, many dimensionality reduction (DR) algorithms have been proposed for visual analysis of multidimensional data. Given a set of n-dimensional observations, such algorithms create a 2D or 3D projection thereof ...