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Machine learning techniques for high dimensional data

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Figure 1.1: An example of embedding learning (taken from [13] but redrawn here):representing a set of 3D data samples in a 2D feature space, while preserving the localneighbour structure of each data sample in the original 3D feature space.
Figure 1.2: An example of image registration for the application area in image mosaicing
Figure 2.1: An example of embedding learning - PCA: (a) for a set of 2D data samplesgenerated from a multivariate normal distribution, a PCA feature space is calculatedso that the corresponding variance is maximised.
Figure 3.3: An example of registering a translation distorted image by NCC - thereference and sensed images are shown in Figure 3.2a and 3.2b, respectively: (c) thedifference between the reference and registered images.
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Outline

Semi-Supervised Learning Techniques Techniques for Nonlinear Extension Area Based Techniques Structure from Motion Image Reconstruction Process Dimensionality Reduction

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