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Local-Topology-Based Scaling for Distance Preserving Dimension Reduction Method to Improve Classification of Biomedical Data-Sets
Content Provider | MDPI |
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Author | Khosla, Karaj Jha, Indra Prakash Kumar, Ajit Kumar, Vibhor |
Copyright Year | 2020 |
Description | Dimension reduction is often used for several procedures of analysis of high dimensional biomedical data-sets such as classification or outlier detection. To improve the performance of such data-mining steps, preserving both distance information and local topology among data-points could be more useful than giving priority to visualization in low dimension. Therefore, we introduce topology-preserving distance scaling (TPDS) to augment a dimension reduction method meant to reproduce distance information in a higher dimension. Our approach involves distance inflation to preserve local topology to avoid collapse during distance preservation-based optimization. Applying TPDS on diverse biomedical data-sets revealed that besides providing better visualization than typical distance preserving methods, TPDS leads to better classification of data points in reduced dimension. For data-sets with outliers, the approach of TPDS also proves to be useful, even for purely distance-preserving method for achieving better convergence. |
Starting Page | 192 |
e-ISSN | 19994893 |
DOI | 10.3390/a13080192 |
Journal | Algorithms |
Issue Number | 8 |
Volume Number | 13 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-08-10 |
Access Restriction | Open |
Subject Keyword | Algorithms Statistics and Probability Dimension Reduction Distance Preserving Local Topology Multidimensional Scaling (mds) |
Content Type | Text |
Resource Type | Article |