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» Semi-supervised nonlinear dimensionality reduction
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COMPGEOM
2009
ACM
15 years 11 months ago
Persistent cohomology and circular coordinates
Nonlinear dimensionality reduction (NLDR) algorithms such as Isomap, LLE and Laplacian Eigenmaps address the problem of representing high-dimensional nonlinear data in terms of lo...
Vin de Silva, Mikael Vejdemo-Johansson
AUTOMATICA
2005
86views more  AUTOMATICA 2005»
15 years 4 months ago
Sensitivity shaping with degree constraint by nonlinear least-squares optimization
This paper presents a new approach to shaping of the frequency response of the sensitivity function. In this approach, a desired frequency response is assumed to be specified at a...
Ryozo Nagamune, Anders Blomqvist
ICML
2004
IEEE
16 years 5 months ago
Generative modeling for continuous non-linearly embedded visual inference
Many difficult visual perception problems, like 3D human motion estimation, can be formulated in terms of inference using complex generative models, defined over high-dimensional ...
Cristian Sminchisescu, Allan D. Jepson
SIGMOD
2006
ACM
125views Database» more  SIGMOD 2006»
16 years 4 months ago
A non-linear dimensionality-reduction technique for fast similarity search in large databases
To enable efficient similarity search in large databases, many indexing techniques use a linear transformation scheme to reduce dimensions and allow fast approximation. In this re...
Khanh Vu, Kien A. Hua, Hao Cheng, Sheau-Dong Lang
IJCAI
2007
15 years 5 months ago
Improving Embeddings by Flexible Exploitation of Side Information
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
Ali Ghodsi, Dana F. Wilkinson, Finnegan Southey