Pages that link to "Item:Q966949"
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The following pages link to A metric multidimensional scaling-based nonlinear manifold learning approach for unsupervised data reduction (Q966949):
Displaying 17 items.
- Dimensionality reduction of collective motion by principal manifolds (Q282231) (← links)
- A supervised non-linear dimensionality reduction approach for manifold learning (Q408078) (← links)
- Manifold elastic net: a unified framework for sparse dimension reduction (Q408616) (← links)
- Nonlinear dimensionality reduction by topologically constrained isometric embedding (Q408807) (← links)
- Nonlinear manifold representations for functional data (Q450014) (← links)
- Inductive manifold learning using structured support vector machine (Q898307) (← links)
- Shape classification by manifold learning in multiple observation spaces (Q903623) (← links)
- Adaptive nonlinear manifolds and their applications to pattern recognition (Q985086) (← links)
- A manifold learning approach to dimensionality reduction for modeling data (Q2214972) (← links)
- Manifold approximation by moving least-squares projection (MMLS) (Q2216655) (← links)
- Latent common manifold learning with alternating diffusion: analysis and applications (Q2330938) (← links)
- PCA-KL: a parametric dimensionality reduction approach for unsupervised metric learning (Q2673341) (← links)
- Megaman: scalable manifold learning in Python (Q2834476) (← links)
- Nonlinear dimensionality reduction methods based on manifold learning (Q2858278) (← links)
- Correlation-based multidimensional scaling for unsupervised subspace learning (Q2902645) (← links)
- Analysis of linear and nonlinear dimensionality reduction methods for gender classification of face images (Q5426601) (← links)
- Computer Vision - ECCV 2004 (Q5713854) (← links)
