Growing Locally Linear Embedding for Manifold Learning
نویسندگان
چکیده
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 embedding, which are intrinsic dimensionality estimation, selection of the number of nearest neighbors, and computational complexity. By embedding the topology learning mechanism in growing neural gas, the proposed GLLE algorithm preserves global topological structures and geometric characteristics of input patterns, which makes the projections more stable. The performed theoretical analysis and experimental simulations show that GLLE results in a faster learning procedure and a lower reconstruction error, which widens the applicability of manifold learning.
منابع مشابه
Short term load forecast by using Locally Linear Embedding manifold learning and a hybrid RBF-Fuzzy network
The aim of the short term load forecasting is to forecast the electric power load for unit commitment, evaluating the reliability of the system, economic dispatch, and so on. Short term load forecasting obviously plays an important role in traditional non-cooperative power systems. Moreover, in a restructured power system a generator company (GENCO) should predict the system demand and its corr...
متن کاملThe Connection Between Manifold Learning and Distance Metric Learning
Manifold Learning learns a low-dimensional embedding of the latent manifold. In this report, we give the definition of distance metric learning, provide the categorization of manifold learning, and describe the essential connection between manifold learning and distance metric learning, with special emphasis on nonlinear manifold learning, including ISOMAP, Laplacian Eigenamp (LE), and Locally ...
متن کاملLocally Linear Embedded Eigenspace Analysis
The existing nonlinear local methods for dimensionality reduction yield impressive results in data embedding and manifold visualization. However, they also open up the problem of how to define a unified projection from new data to the embedded subspace constructed by the training samples. Thinking globally and fitting locally, we present a new linear embedding approach, called Locally Embedded ...
متن کاملAlgorithms for manifold learning
Manifold learning is a popular recent approach to nonlinear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high; though each data point consists of perhaps thousands of features, it may be described as a function of only a few underlying parameters. That is, the data points are actually samples from a low-d...
متن کاملPiecewise-Linear Manifold Learning
The need to reduce the dimensionality of a dataset whilst retaining inherent manifold structure is key in many pattern recognition, machine learning and computer vision tasks. This process is often referred to as manifold learning since the structure is preserved during dimensionality reduction by learning the intrinsic low-dimensional manifold that the data lies on. Since the inception of mani...
متن کامل