Locally linear embedding (Lle) is a powerful approach for mapping high-dimensional data nonlinearly to a lower-dimensional space. However, when the training examples are not densely sampled, Lle often returns invalid results. In this paper, the Nle (Neighbor Line-based Lle) approach is proposed, which generates some virtual examples with the help of neighbor line such that the Lle learning can ...

A glycoprotein has been isolated from the colonic lavages of healthy individuals that is immunologically equivalent to carcinoembryonic antigen purified from tumor tissue. The NH2-terminal sequence of the glycoprotein from normal colon lavages is Lys-Leu-Thr-lle-Glu-Ser-Thr-Pro-Phe-(Asn)-Val-Ala-Glu-Gly-Lys-Glu-Val-(Leu,lle)-(Leu,lle)-(Leu,lle)-Val-(His,Arg?)-?-(Leu,lle). This is homologous to ...

This paper introduces a new concept of LLE eigenface modelled by local linear embedding (LLE), and compares it with the traditional PCA eigenface from principle component analysis (PCA) on pose identity and face identity recognition through face classification. LLE eigenface is found outperforming PCA eigenface on the discrimination/recogntion of both face identity and pose identity. The superi...

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, ...

Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. In this paper, we respond intriguing learning-related question -- if leveraging both accessible unpaired over/underexposed images high-level semantic guidance, can improve performance cutting-edge LLE models? Here, propose an effective semantica...

Locally Linear Embedding (LLE) is a widely used non-linear dimensionality reduction (NLDR) method that projects multi-dimensional data into a low-dimensional embedding space while attempting to preserve object adjacencies from the original high-dimensional feature space. A limitation of LLE, however, is the presence of free parameters, changing the values of which may dramatically change the lo...

Nonlinear dimensionality reduction is the problem of retrieving a low-dimensional representation of a manifold that is embedded in a high-dimensional observation space. Locally Linear Embedding (LLE), a prominent dimensionality reduction technique is an unsupervised algorithm; as such, it is not possible to guide it toward modes of variability that may be of particular interest. This paper prop...