We present Discriminant Pairwise Local Embeddings. (DPLE), a manifold learning algorithm inspired by LPP [5]. The main idea is to learn a discriminant ...
This paper introduces Discriminant Pairwise Local Embeddings (DPLE) a supervised dimensionality reduction technique that generates structure preserving ...
This paper introduces Discriminant Pairwise Local Embeddings (DPLE) a supervised dimensionality reduction technique that generates structure preserving ...
This paper introduces Discriminant Pairwise Local Embeddings (DPLE) a supervised dimensionality reduction technique that generates structure preserving ...
Compared with traditional graph-embedding methods, the features obtained by GPE could more accurately discriminate, both in classification and clustering tasks.
Based on this idea, we introduce a local pairwise linear discriminant anal- ysis (LPLDA) algorithm to emphasize local structure instead of global structure on ...
Sep 15, 2022 · Linear discriminant analysis (LDA) is one of the most effective and popular methods to reduce the dimensionality of data with Gaussian ...
In this paper we attempt to learn feature descrip- tors in a more unstructured fashion using linear discriminant embedding (LDE). We present three main ...
Missing: Pairwise | Show results with:Pairwise
Sep 18, 2023 · ... local discriminant embeddings for exploring the intrinsic structure of the non-Gaussian labeled data, i.e., the submanifold structure. Then ...
Oct 22, 2024 · We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification.