Add to ORKGAbstract. We present a fast alternative for the Isomap algorithm. A set of quantizers is ¯t to the data and a neighborhood structure based on the competitive Hebbian rule is imposed on it. This structure is used to obtain low-dimensional description of the data by means of comput-ing geodesic distances and multi dimensional scaling. The quantization allows for faster processing of the data. The speed-up as compared to Isomap is roughly quadratic in the ratio between the number of quan-tizers and the number of data points. The quantizers and neighborhood structure are use to map the data to the low dimensional space.
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-...
Abstract Besides the linear methods above mentioned, several nonlinear embedding methods have been N...
The fundamental problem of distance geometry consists in finding a realization of a given weighted g...
peer reviewedWe present a fast alternative for the Isomap algorithm. A set of quantizers is fit to t...
Most nonlinear dimensionality reduction approaches such as Isomap heavily depend on the neighborhood...
Dimensionality reduction in the machine learning field mitigates the undesired properties of high-di...
The problem addressed in nonlinear dimensionality reduction, is to find lower dimensional configurat...
Abstract—When performing visualization and classification, people often confront the problem of dime...
Abstract—Isomap is a well-known nonlinear dimensionality reduction (DR) method, aiming at preserving...
Manifold learning and nonlinear dimensionality reduction addresses the problem of detecting possibly...
International audienceThe fundamental problem of distance geometry consists in finding a realization...
Abstract — Understanding the structure of multidimensional patterns, especially in unsupervised case...
Numerous methods or algorithms have been designed to solve the problem of nonlinear dimensionality r...
Numerous methods or algorithms have been designed to solve the problem of nonlinear dimensionality r...
We present a new algorithm for nonlinear dimensionality reduction that consistently uses global info...
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-...
Abstract Besides the linear methods above mentioned, several nonlinear embedding methods have been N...
The fundamental problem of distance geometry consists in finding a realization of a given weighted g...
peer reviewedWe present a fast alternative for the Isomap algorithm. A set of quantizers is fit to t...
Most nonlinear dimensionality reduction approaches such as Isomap heavily depend on the neighborhood...
Dimensionality reduction in the machine learning field mitigates the undesired properties of high-di...
The problem addressed in nonlinear dimensionality reduction, is to find lower dimensional configurat...
Abstract—When performing visualization and classification, people often confront the problem of dime...
Abstract—Isomap is a well-known nonlinear dimensionality reduction (DR) method, aiming at preserving...
Manifold learning and nonlinear dimensionality reduction addresses the problem of detecting possibly...
International audienceThe fundamental problem of distance geometry consists in finding a realization...
Abstract — Understanding the structure of multidimensional patterns, especially in unsupervised case...
Numerous methods or algorithms have been designed to solve the problem of nonlinear dimensionality r...
Numerous methods or algorithms have been designed to solve the problem of nonlinear dimensionality r...
We present a new algorithm for nonlinear dimensionality reduction that consistently uses global info...
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-...
Abstract Besides the linear methods above mentioned, several nonlinear embedding methods have been N...
The fundamental problem of distance geometry consists in finding a realization of a given weighted g...