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Ideal regularization for learning kernels from labels
TLDR
In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. Expand
  • 19
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Out-of-Sample Extensions for Non-Parametric Kernel Methods
TLDR
In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. Expand
  • 10
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Supervised kernel nonnegative matrix factorization for face recognition
TLDR
Nonnegative matrix factorization (NMF) is a promising algorithm for dimensionality reduction and local feature extraction. Expand
  • 47
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Nonlinear nonnegative matrix factorization based on Mercer kernel construction
TLDR
In this paper, we show that nonlinear semi-NMF cannot extract the localized components which offer important information in object recognition. Expand
  • 41
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Incremental Nonnegative Matrix Factorization for Face Recognition
Nonnegative matrix factorization (NMF) is a promising approach for local feature extraction in face recognition tasks. However, there are two major drawbacks in almost all existing NMF-based methods.Expand
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A fractional order derivative based active contour model for inhomogeneous image segmentation
TLDR
A new hybrid method for inhomogeneous image segmentation, which incorporates image gradient, local environment and global information into a framework, called adaptive-weighting active contour model. Expand
  • 15
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A novel constraint non-negative matrix factorization criterion based incremental learning in face recognition
TLDR
We proposed a novel constraint block NMF (CBNMF) method, which is based on a new constraint NMF criterion and our previous block technique in NMF. Expand
  • 2
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A novel discriminant criterion based on feature fusion strategy for face recognition
TLDR
This paper proposes a novel discriminant criterion using Feature Fusion Strategy (FFS), which nonlinearly combines both Euclidean and manifold structures in the face pattern space. Expand
  • 27
A Novel Framework for Learning Geometry-Aware Kernels
TLDR
The data from real world usually have nonlinear geometric structure, which are often assumed to lie on or close to a low-dimensional manifold in a high-dimensional space. Expand
  • 18
Semi-supervised discriminant analysis method for face recognition
TLDR
This paper proposes a novel Semi-Supervised Discriminant Analysis (SSDA) criterion via nonlinearly combining the global feature and local feature. Expand
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