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ICONIP
2004
15 years 3 months ago
Non-linear Dimensionality Reduction by Locally Linear Isomaps
Algorithms for nonlinear dimensionality reduction (NLDR) find meaningful hidden low-dimensional structures in a high-dimensional space. Current algorithms for NLDR are Isomaps, Loc...
Ashutosh Saxena, Abhinav Gupta, Amitabha Mukerjee
ICML
2010
IEEE
15 years 3 months ago
The Elastic Embedding Algorithm for Dimensionality Reduction
We propose a new dimensionality reduction method, the elastic embedding (EE), that optimises an intuitive, nonlinear objective function of the low-dimensional coordinates of the d...
Miguel Á. Carreira-Perpiñán
TNN
2008
129views more  TNN 2008»
15 years 1 months ago
Data Visualization and Dimensionality Reduction Using Kernel Maps With a Reference Point
In this paper, a new kernel-based method for data visualization and dimensionality reduction is proposed. A reference point is considered corresponding to additional constraints ta...
Johan A. K. Suykens
IEEEVAST
2010
14 years 8 months ago
DimStiller: Workflows for dimensional analysis and reduction
DimStiller is a system for dimensionality reduction and analysis. It frames the task of understanding and transforming input dimensions as a series of analysis steps where users t...
Stephen Ingram, Tamara Munzner, Veronika Irvine, M...
AICCSA
2008
IEEE
276views Hardware» more  AICCSA 2008»
15 years 4 months ago
Effects of dimensionality reduction techniques on time series similarity measurements
Time Series are ubiquitous, hence, similarity search is one of the biggest challenges in the area of mining time series data. This is due to the vast data size, number of sequence...
Ghazi Al-Naymat, Javid Taheri