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ICML
2007
IEEE
14 years 5 months ago
Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps
Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spect...
Samuel Gerber, Tolga Tasdizen, Ross T. Whitaker
NIPS
2007
13 years 5 months ago
People Tracking with the Laplacian Eigenmaps Latent Variable Model
Reliably recovering 3D human pose from monocular video requires models that bias the estimates towards typical human poses and motions. We construct priors for people tracking usi...
Zhengdong Lu, Miguel Á. Carreira-Perpi&ntil...
CVPR
2010
IEEE
14 years 5 days ago
Parametric Dimensionality Reduction by Unsupervised Regression
We introduce a parametric version (pDRUR) of the recently proposed Dimensionality Reduction by Unsupervised Regression algorithm. pDRUR alternately minimizes reconstruction error ...
Miguel Carreira-perpinan, Zhengdong Lu
NIPS
2007
13 years 5 months ago
Locality and low-dimensions in the prediction of natural experience from fMRI
Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into the complex functioning of the human brain, detailing the hemodynamic activity of thousands of voxels d...
Francois Meyer, Greg Stephens
CVPR
2007
IEEE
14 years 6 months ago
Biased Manifold Embedding: A Framework for Person-Independent Head Pose Estimation
The estimation of head pose angle from face images is an integral component of face recognition systems, human computer interfaces and other human-centered computing applications....
Vineeth Nallure Balasubramanian, Jieping Ye, Sethu...