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Subspace selection is widely adopted in many areas of pattern recognition. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), is a successful method for subspace selection, which can significantly reduce the class separation problem. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained...
Fisher's linear discriminant analysis (FLDA) is one of the most well-known linear subspace selection methods. However, FLDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. Recent results show that maximizing the geometric mean or harmonic mean of Kullback-Leibler (KL) divergences of class pairs can significantly reduce this problem. In this...
In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler...
In this paper, we try to identify and quantify the chemical species present on the surface of planet Mars with the help of hyperspectral images provided by the instrument OMEGA. For this purpose, we suppose that the spectrum of each pixel is a linear mixture of the spectra of different endmembers. From this linear mixture hypothesis, our work is divided into two steps. Firstly, we propose a new unsupervised...
Accurate head pose tracking is a key issue for indoor augmented reality systems. This paper proposes a novel approach to track head pose of indoor users using sensor fusion. The proposed approach utilizes a track-to-track fusion framework composed of extended Kalman filters and fusion filter to fuse the poses from the two complementary tracking modes of inside-out tracking (IOT) and outside-in tracking...
In this paper, we try to identify and quantify the chemical species present on the surface of planet Mars with the help of hyperspectral images provided by the instrument OMEGA (Bibring et al., 2004). For this purpose, we suppose that the spectrum of each pixel is a linear mixture of the spectra of different endmembers. From this linear mixture hypothesis, our work is divided into two steps. Firstly,...
Semi-tied covariance (STC) is applied widely in speech recognition due to its feature de-correlation ability. Solving the transform matrices of STC is a nonlinear optimization problem. Gales proposed an efficient method by iteratively updating a row of transform matrices. However, it needs to solve cofactors of elements of a matrix row in two layers of loops. Directly solving them is very time-consuming...
This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence by using correspondence information to place the nodes of each of a set of graphs in a standard reference order. Using the correspondences order, we convert the adjacency matrices to long-vectors and compute the long-vector...
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