Abstract
This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
Highlights
Techniques for dimensionality reduction have attracted much attention in many fields such as machine learning and data mining [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
Dimensionality reduction methods are used for mapping high-dimensional observations into desired low-dimensional space while preserving the features hidden in the original space
Improved TRN (ITRN)-RBF is used for visualization and feature extraction, and is compared with others including methods based on Topology Representing Network (TRN) and classical dimensionality reduction methods such as ISOMAP, L-ISOMAP and Principal Component Analysis (PCA)
Summary
Techniques for dimensionality reduction have attracted much attention in many fields such as machine learning and data mining [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]. Dimensionality reduction methods are used for mapping high-dimensional observations into desired low-dimensional space while preserving the features hidden in the original space. Classical MDS finds a low-dimensional embedding of patterns with distances in the target space that reflects dissimilarities in the original sample. Both PCA and MDS cannot disclose nonlinearly embedded manifolds because they operate on Euclidean distances. To overcome this limitation, many nonlinear methods have been proposed. Linear Embedding (LLE) [22] maps high-dimensional original data feature space into a single global coordinate system of PLOS ONE | DOI:10.1371/journal.pone.0131631. Linear Embedding (LLE) [22] maps high-dimensional original data feature space into a single global coordinate system of PLOS ONE | DOI:10.1371/journal.pone.0131631 July 10, 2015
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
