Geoscience Reference
In-Depth Information
Related papers:
[
17
]
Title:
Manifold Regularization Demo
Author:
Mike Rainey (University of Chicago)
Description:
Graphical demonstration of manifold regularization on toy data sets. Allows users to
manipulate graph and regularization parameters to explore the algorithm in detail.
Related papers:
[
17
]
Title:
Similarity Graph Demo
Authors:
Matthias Hein (Saarland University), Ulrike von Luxburg (Max Planck Institute for
Biological Cybernetics)
Description:
Matlab-based graphical user interface for exploring similarity graphs. These graphs
can be used for semi-supervised learning, spectral clustering, and other tasks. Also includes several
commonly used toy data sets.
Related papers:
See references in Chapter 5.
Title:
Maximum Variance Unfolding
Author:
Kilian Q. Weinberger (Yahoo! Research)
Description:
Implements variations of the dimensionality reduction technique known as maximum
variance unfolding. This is a graph-based, spectral method that can use unlabeled data in a
preprocessing step for classification or other tasks.
Related papers:
[
148
]
Title:
SGT
light
(Spectral Graph Transducer)
Author:
Thorsten Joachims (Cornell University)
Description:
Implements the spectral graph transducer, which is a transductive learning method
based on a combination of minimum cut problems and spectral graph theory.
Related papers:
[
90
]
Title:
SemiL
Authors:
Te-Ming Huang (INRIX), Vojislav Kecman (University of Auckland)
Description:
Graph-based semi-supervised learning implementations optimized for large-scale
data problems. The code combines and extends the seminal works in graph-based learning.
