<p>(a) The usage of attribute types in similarity-based scanning as a proportion of the trial's fixations sequence. (b) The changing proportion of trials in which dimension-reduction was used. The proportions are calculated as a function of the fixation position within a trial. The proportion on fixation <i>x</i> is calculated by counting the trials that have a dimension-reduction block that include fixation <i>x</i>. The lengths of blocks from word trials are also normalized to match the length scale of picture trials.</p
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
<p>(a) The usage of attribute types in similarity-based scanning as a proportion of the trial's fixa...
Dimension reduction is the process of keeping only those dimensions in a dataset which are important...
When data objects that are the subject of analysis using machine learning techniques are described b...
Abstract- Classification is undoubtedly gaining major importance in the fields of machine learning, ...
<p>The plot shows the coefficient of determination (fraction of explained variance) as a function of...
A prominent difficulty facing researchers is the visualization of high dimensional data. Several dim...
Values indicate the mean similarity index (with SD in parentheses) across the six tasks (Human Visua...
This thesis centers around dimensionality reduction and its usage on landmark-type data which are of...
<p>(a) Fixation probability: the likelihood that the dimension was fixated at least once during each...
textabstractRepresenting the information in a data set in a concise way is an important part of data...
<p>Comparison of full and reduced models including busts. Dotted lines represent 95% intervals for r...
Whether the presence of bidimensionality has any effect on the adaptive recalibration of test items ...
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
<p>(a) The usage of attribute types in similarity-based scanning as a proportion of the trial's fixa...
Dimension reduction is the process of keeping only those dimensions in a dataset which are important...
When data objects that are the subject of analysis using machine learning techniques are described b...
Abstract- Classification is undoubtedly gaining major importance in the fields of machine learning, ...
<p>The plot shows the coefficient of determination (fraction of explained variance) as a function of...
A prominent difficulty facing researchers is the visualization of high dimensional data. Several dim...
Values indicate the mean similarity index (with SD in parentheses) across the six tasks (Human Visua...
This thesis centers around dimensionality reduction and its usage on landmark-type data which are of...
<p>(a) Fixation probability: the likelihood that the dimension was fixated at least once during each...
textabstractRepresenting the information in a data set in a concise way is an important part of data...
<p>Comparison of full and reduced models including busts. Dotted lines represent 95% intervals for r...
Whether the presence of bidimensionality has any effect on the adaptive recalibration of test items ...
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...