In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured ...

Abstract. In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding.

Feb 18, 2015 · In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for ...

In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured ...

In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured ...

Bibliographic details on Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data.

Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data. Y. Guo, J. Gao, and P. Kwan.

May 9, 2016 · We extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method.

Nov 22, 2020 · The main idea of LLE is using the same reconstruc- tion weights in the lower dimensional embedding space as in the high dimensional input space.

We propose a local nonlinear dimensionality reduction method named Vec2vec, which employs a neural network with only one hidden layer to reduce the ...