2,882 research outputs found
Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images
In this paper, we propose an L1 normalized graph based dimensionality
reduction method for Hyperspectral images, called as L1-Scaling Cut (L1-SC).
The underlying idea of this method is to generate the optimal projection matrix
by retaining the original distribution of the data. Though L2-norm is generally
preferred for computation, it is sensitive to noise and outliers. However,
L1-norm is robust to them. Therefore, we obtain the optimal projection matrix
by maximizing the ratio of between-class dispersion to within-class dispersion
using L1-norm. Furthermore, an iterative algorithm is described to solve the
optimization problem. The experimental results of the HSI classification
confirm the effectiveness of the proposed L1-SC method on both noisy and
noiseless data.Comment: European Signal Processing Conference 201
Large Margin Image Set Representation and Classification
In this paper, we propose a novel image set representation and classification
method by maximizing the margin of image sets. The margin of an image set is
defined as the difference of the distance to its nearest image set from
different classes and the distance to its nearest image set of the same class.
By modeling the image sets by using both their image samples and their affine
hull models, and maximizing the margins of the images sets, the image set
representation parameter learning problem is formulated as an minimization
problem, which is further optimized by an expectation -maximization (EM)
strategy with accelerated proximal gradient (APG) optimization in an iterative
algorithm. To classify a given test image set, we assign it to the class which
could provide the largest margin. Experiments on two applications of
video-sequence-based face recognition demonstrate that the proposed method
significantly outperforms state-of-the-art image set classification methods in
terms of both effectiveness and efficiency
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