New supervised locally linear embedding for dimensionality reduction using distance metric learning

Title:

New supervised locally linear embedding for dimensionality reduction using distance metric learning

Creator:

Yang, B., Xiang, M., Zhang, Y. P., Pang, S. M., and Li, X.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:ebaea2eb-804c-4b79-bd08-59cc90ae2ffb
uuid:ebaea2eb-804c-4b79-bd08-59cc90ae2ffb
doi:10.14311/NNW.2016.26.026

Subject:

dimensionality reduction, manifold learning, locally linear embedding, and deistance metric learning

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Feature reduction is an important issue in pattern recognition. Lower feature dimensionality could reduce the complexity and enhance the generalization ability of classifiers. In this paper we propose a new supervised dimensionality reduction method based on Locally Linear Embedding and Distance Metric Learning. First, in order to increase the interclass separability, a linear discriminant transformation learnt from distance metric learning is used to map the original data points to a new space. Then Locally Linear Embedding is adopted to reduce the dimensionality of data points. This process extends the traditional unsupervised Locally Linear Embedding to supervised scenario in a clear and natural way. In addition, it can also be seen as a general framework for developing new supervised dimensionality reduction algorithms by utilizing corresponding unsupervised methods. Extensive classification experiments performed on some real-world and artificial datasets show that the proposed method can achieve comparable to or even better results over other state-of-the-art dimensionality reduction methods.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:5

Harvested from:

CDK

Metadata only:

false