Add to ORKGVisual category recognition is a difficult task of significant interest to the machine learning and vision community. One of the principal hurdles is the high dimensional feature space. This paper evaluates several linear and non-linear dimensionality reduction techniques. A novel evaluation metric, the rényi entropy of the inter-vector euclidean distance distribution, is introduced. This information theoretic measure judges the techniques on their preservation of structure in lower-dimensional sub-space. The popular dataset, Caltech-101 is utilized in the experiments. The results indicate that the techniques which preserve local neighborhood structure performed best amongst the techniques evaluated in this paper. © 2011 EURASIP
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Visual category recognition is a difficult task of significant interest to the machine learning and ...
Dimensionality reduction (DR) aims to reveal salient properties of high-dimensional (HD) data in a l...
Abstract—We are dealing with large-scale high-dimensional image data sets requiring new approaches f...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
We are dealing with large-scale high-dimensional image data sets requiring new approaches for data ...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
There are many methods for determining the Classification Accuracy. In this paper significance of En...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
This thesis deals with the problem of estimating structure in data due to the semantic relations bet...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Visual category recognition is a difficult task of significant interest to the machine learning and ...
Dimensionality reduction (DR) aims to reveal salient properties of high-dimensional (HD) data in a l...
Abstract—We are dealing with large-scale high-dimensional image data sets requiring new approaches f...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
We are dealing with large-scale high-dimensional image data sets requiring new approaches for data ...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
There are many methods for determining the Classification Accuracy. In this paper significance of En...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
This thesis deals with the problem of estimating structure in data due to the semantic relations bet...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...