31 research outputs found
Locality Preserving Projections for Grassmann manifold
Learning on Grassmann manifold has become popular in many computer vision
tasks, with the strong capability to extract discriminative information for
imagesets and videos. However, such learning algorithms particularly on
high-dimensional Grassmann manifold always involve with significantly high
computational cost, which seriously limits the applicability of learning on
Grassmann manifold in more wide areas. In this research, we propose an
unsupervised dimensionality reduction algorithm on Grassmann manifold based on
the Locality Preserving Projections (LPP) criterion. LPP is a commonly used
dimensionality reduction algorithm for vector-valued data, aiming to preserve
local structure of data in the dimension-reduced space. The strategy is to
construct a mapping from higher dimensional Grassmann manifold into the one in
a relative low-dimensional with more discriminative capability. The proposed
method can be optimized as a basic eigenvalue problem. The performance of our
proposed method is assessed on several classification and clustering tasks and
the experimental results show its clear advantages over other Grassmann based
algorithms.Comment: Accepted by IJCAI 201
Online Informative Path Planning for Active Information Gathering of a 3D Surface
This paper presents an online informative path planning approach for active
information gathering on three-dimensional surfaces using aerial robots. Most
existing works on surface inspection focus on planning a path offline that can
provide full coverage of the surface, which inherently assumes the surface
information is uniformly distributed hence ignoring potential spatial
correlations of the information field. In this paper, we utilize manifold
Gaussian processes (mGPs) with geodesic kernel functions for mapping surface
information fields and plan informative paths online in a receding horizon
manner. Our approach actively plans information-gathering paths based on recent
observations that respect dynamic constraints of the vehicle and a total flight
time budget. We provide planning results for simulated temperature modeling for
simple and complex 3D surface geometries (a cylinder and an aircraft model). We
demonstrate that our informative planning method outperforms traditional
approaches such as 3D coverage planning and random exploration, both in
reconstruction error and information-theoretic metrics. We also show that by
taking spatial correlations of the information field into planning using mGPs,
the information gathering efficiency is significantly improved.Comment: 7 pages, 7 figures, to be published in 2021 IEEE International
Conference on Robotics and Automation (ICRA
Weighted adjacent matrix for K-means clustering
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