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PKDD
2010
Springer
160views Data Mining» more  PKDD 2010»
15 years 4 months ago
Sparse Unsupervised Dimensionality Reduction Algorithms
Abstract. Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we sho...
Wenjun Dou, Guang Dai, Congfu Xu, Zhihua Zhang
CGF
2010
144views more  CGF 2010»
15 years 6 months ago
Dynamic Multi-View Exploration of Shape Spaces
Statistical shape modeling is a widely used technique for the representation and analysis of the shapes and shape variations present in a population. A statistical shape model mod...
Stef Busking, Charl P. Botha, Frits H. Post
ICML
2007
IEEE
16 years 7 months ago
Map building without localization by dimensionality reduction techniques
This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is...
Takehisa Yairi
ESANN
2004
15 years 7 months ago
Dimensionality reduction and classification using the distribution mapping exponent
: Probability distribution mapping function, which maps multivariate data distribution to the function of one variable, is introduced. Distributionmapping exponent (DME) is somethi...
Marcel Jirina
ICONIP
2004
15 years 7 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