Learning Pattern Transformation Manifolds with Parametric Atom Selection



Learning pattern transformation manifolds for classification
Vural, Elif (2013-02-21)
Manifold models provide low-dimensional representations that are useful for analyzing and classifying data in a transformation-invariant way. In this paper we study the problem of jointly building multiple pattern transformation manifolds from a collection of image sets, where each set consists of observations from a class of geometrically transformed signals. We build the manifolds such that each manifold approximates a different signal class. Each manifold is characterized by a representative pattern that...
Learning Smooth Pattern Transformation Manifolds
Vural, Elif (2013-04-01)
Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals. To construct a manifold, we build a representative pattern whose transformations accurately fit various input images. We examine two objectives of the manifold-building problem, namely, approximation a...
Learning semi-supervised nonlinear embeddings for domain-adaptive pattern recognition
Vural, Elif (null; 2019-05-20)
We study the problem of learning nonlinear data embeddings in order to obtain representations for efficient and domain-invariant recognition of visual patterns. Given observations of a training set of patterns from different classes in two different domains, we propose a method to learn a nonlinear mapping of the data samples from different domains into a common domain. The nonlinear mapping is learnt such that the class means of different domains are mapped to nearby points in the common domain in order to...
Learning Parameters of ptSTL Formulas with Backpropagation
Ketenci, Ahmet; Aydın Göl, Ebru (2020-01-01)
In this paper, a backpropagation based algorithm is presented to learn parameters of past time Signal Temporal Logic (ptSTL) formulas. A differentiable weight matrix over the parameter values and a loss function based on the mismatch value of the corresponding formulas over the labeled dataset are used in the algorithm. Analysis over a sample dataset shows that the algorithm solves the ptSTL parameter synthesis problem in an efficient way.
Learning sequences of compatible actions among agents
Polat, Faruk (2002-03-01)
Action coordination in multiagent systems is a difficult task especially in dynamic environments. If the environment possesses cooperation, least communication, incompatibility and local information constraints, the task becomes even more difficult. Learning compatible action sequences to achieve a designated goal under these constraints is studied in this work. Two new multiagent learning algorithms called QACE and NoCommQACE are developed. To improve the performance of the QACE and NoCommQACE algorithms f...
Citation Formats
E. Vural, “Learning Pattern Transformation Manifolds with Parametric Atom Selection,” 2011, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/76770.