a method based on the genetic algorithm (ga) was developed to study the phase behavior of multicomponent and multiphase systems. upon application of the ga to the thermodynamic models which are commonly used to study the vle, vlle and lle phase equilibria, the physically meaningful values for the binary interaction parameters (bip) of the models were obtained. using the method proposed in this ...

Locally Linear Embedding (LLE) algorithm is the first classic nonlinear manifold learning algorithm based on the local structure information about the data set, which aims at finding the low-dimension intrinsic structure lie in high dimensional data space for the purpose of dimensionality reduction. One deficiency appeared in this algorithm is that it requires users to give a free parameter k w...

background: increasing frame rate is a challenging issue for better interpretation of medical images and diagnosis based on tracking the small transient motions of myocardium and valves in real time visualization. methods: in this paper, manifold learning algorithm is applied to extract the nonlinear embedded information about echocardiography images from the consecutive images in two dimension...

This paper considers a recently proposed method for unsupervised learning and dimensionality reduction, locally linear embedding (LLE). LLE computes a compact representation of highdimensional data combining the major advantages of linear methods (computational efficiency, global optimality, and flexible asymptotic convergence guarantees) with the advantages of non-linear approaches (flexibilit...

Locally Linear embedding (LLE) is a popular dimension reduction method. In this paper, we systematically improve the two main steps of LLE: (A) learning the graph weights W, and (B) learning the embedding Y. We propose a sparse nonnegative W learning algorithm. We propose a weighted formulation for learning Y and show the results are identical to normalized cuts spectral clustering. We further ...

In this paper, the main application image processing, manifold learning and the method of Gaussian mixture model for dimensionality reduction and cluster analysis, the image color information are all studied. First, the color data access algorithm is introduced, secondly, the manifold learning in the local linear embedding (LLE) algorithm is used in color analysis; then the results of an evalua...

The locally linear embedding (LLE) algorithm belongs to a group of manifold learning methods that not only merely reduce data dimensionality, but also attempt to discover a true low dimensional structure of the data. In this paper, we propose an incremental version of LLE and experimentally demonstrate its advantages in terms of topology preservation. Also compared to the original (batch) LLE, ...

Locally linear embedding is a popular manifold learning algorithm for nonlinear dimensionality reduction. However, the success of LLE depends greatly on an input parameter neighborhood size, and it is still an open problem how to find the optimal value for it. This paper focuses on this parameter, proposes that it should be self-tuning according to local density not a uniform value for all the ...