Sökning: "Manifold Learning"
Visar resultat 1 - 5 av 20 avhandlingar innehållade orden Manifold Learning.
1. Manifold Learning in Computational Biology
Sammanfattning : This thesis deals with manifold learning techniques and their application in gene expression data analysis. Manifold learning is the study of methods that aim to infer geometrical structure from data sampled from manifolds, enabling nonlinear solutions to various machine learning tasks. LÄS MER
2. Manifold learning and representations for image analysis and visualization
Sammanfattning : We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. LÄS MER
3. Stochastic Modeling for Video Object Tracking and Online Learning: manifolds and particle filters
Sammanfattning : Classical visual object tracking techniques provide effective methods when parameters of the underlying process lie in a vector space. However, various parameter spaces commonly occurring in visual tracking violate this assumption. LÄS MER
4. Regression on Manifolds with Implications for System Identification
Sammanfattning : The trend today is to use many inexpensive sensors instead of a few expensive ones, since the same accuracy can generally be obtained by fusing several dependent measurements. It also follows that the robustness against failing sensors is improved. As a result, the need for high-dimensional regression techniques is increasing. LÄS MER
5. Manifolds in Image Science and Visualization
Sammanfattning : A Riemannian manifold is a mathematical concept that generalizes curved surfaces to higher dimensions, giving a precise meaning to concepts like angle, length, area, volume and curvature. A glimpse of the consequences of a non-flat geometry is given on the sphere, where the shortest path between two points – a geodesic – is along a great circle. LÄS MER