Manifold-Based Reinforcement Learning via Locally Linear Reconstruction
نویسندگان
چکیده
منابع مشابه
Data-based Manifold Reconstruction via Tangent Bundle Manifold Learning
The goal of Manifold Learning (ML) is to find a description of low-dimensional structure of an unknown q-dimensional manifold embedded in high-dimensional ambient Euclidean space R p , q < p, from their finite samples. There are a variety of formulations of the problem. The methods of Manifold Approximation (MA) reconstruct (estimate) the manifold but don’t find a low-dimensional parameterizati...
متن کاملGrowing Locally Linear Embedding for Manifold Learning
Locally linear embedding is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. This paper proposes a new manifold learning method, which is based on locally linear embedding and growing neural gas and is termed growing locally linear embedding (GLLE). GLLE overcomes the major limitations of the original locally linear emb...
متن کاملTreating Epilepsy by Reinforcement Learning Via Manifold-Based Simulation
The ability to take intelligent actions in real-world domains is a goal of great interest in the machine learning community. Unfortunately, the real-world is filled with systems that can be partially observed but cannot, as yet, be described by first principle models. Moreover, the traditional paradigm of direct interaction with the environment used in reinforcement learning is often prohibitiv...
متن کاملLocally Linear Landmarks for Large-Scale Manifold Learning
Spectral methods for manifold learning and clustering typically construct a graph weighted with affinities (e.g. Gaussian or shortest-path distances) from a dataset and compute eigenvectors of a graph Laplacian. With large datasets, the eigendecomposition is too expensive, and is usually approximated by solving for a smaller graph defined on a subset of the points (landmarks) and then applying ...
متن کاملBayesian Manifold Learning: The Locally Linear Latent Variable Model
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. The model allows straightforward variational optimisation of the posterior distribution on coordinates and locally lin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2017
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2015.2505084