{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:07:45Z","timestamp":1760238465030,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a13080192","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T05:07:23Z","timestamp":1597036043000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Local-Topology-Based Scaling for Distance Preserving Dimension Reduction Method to Improve Classification of Biomedical Data-Sets"],"prefix":"10.3390","volume":"13","author":[{"given":"Karaj","family":"Khosla","sequence":"first","affiliation":[{"name":"Department of Computer Science, Guru Tegh Bahadur Institute of Technology, New Delhi 110064, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indra Prakash","family":"Jha","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, IIIT Delhi, Okhla Phase-3, New Delhi 110020, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajit","family":"Kumar","sequence":"additional","affiliation":[{"name":"Adobe, Block-A, Sector-321, Noida 201304, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9523-9422","authenticated-orcid":false,"given":"Vibhor","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, IIIT Delhi, Okhla Phase-3, New Delhi 110020, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/T-C.1969.222678","article-title":"A Nonlinear Mapping for Data Structure Analysis","volume":"100","author":"Sammon","year":"1969","journal-title":"IEEE T. Comput."},{"key":"ref_2","first-page":"2579","article-title":"Visualizing High-Dimensional Data Using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lee, J.A., and Verleysen, M. (2007). Nonlinear Dimensionality Reduction, Springer.","DOI":"10.1007\/978-0-387-39351-3"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neucom.2012.02.034","article-title":"Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences","volume":"90","author":"Bunte","year":"2012","journal-title":"Neurocomputing"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gorban, A.N., Balzs, K., Wunsch, D.C., and Zinovyev, A. (2008). Principal Manifolds for Data Visualization and Dimension Reduction, Springer. [1st ed.].","DOI":"10.1007\/978-3-540-73750-6"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dzemyda, G., Kurasova, O., and Zilinskas, J. (2013). Multidimensional Data Visualization, Springer.","DOI":"10.1007\/978-1-4419-0236-8"},{"key":"ref_8","unstructured":"Silva, V.D., and Tenenbaum, J.B. (2003). Global versus Local Methods in Nonlinear Dimensionality Reduction. Advances in Neural Information Processing Systems 15, The MIT Press."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1109\/TPAMI.2018.2851513","article-title":"Outlier Detection for Robust Multi-Dimensional Scaling","volume":"41","author":"Blouvshtein","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41576-018-0088-9","article-title":"Challenges in unsupervised clustering of single-cell RNA-seq data","volume":"20","author":"Kiselev","year":"2019","journal-title":"Nat. Rev. Genet."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/pmic.201600267","article-title":"Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis","volume":"17","author":"Su","year":"2017","journal-title":"Proteomics"},{"key":"ref_12","unstructured":"Sorzano, C.O.S., Vargas, J., and Montano, A.P. (2014). A survey of dimensionality reduction techniques. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhang, L., Liu, Z., Nenghai, Y., and Li, H. (2005, January 23\u201326). Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method. Proceedings of the 2005 International Conference on Advances in Intelligent Computing\u2014Volume Part I, Hefei, China.","DOI":"10.1007\/11538059_13"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.cmpb.2017.02.019","article-title":"A two-stage variable selection and classification approach for Parkinson\u2019s disease detection by using voice recording replications","volume":"142","author":"Naranjo","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ijmedinf.2018.03.003","article-title":"SCADI: A standard dataset for self-care problems classification of children with physical and motor disability","volume":"114","author":"Zarchi","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0129126","article-title":"Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome","volume":"10","author":"Higuera","year":"2015","journal-title":"PLoS ONE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1038\/ng.3818","article-title":"Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors","volume":"49","author":"Li","year":"2017","journal-title":"Nat. Genet."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3068335","article-title":"DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN","volume":"42","author":"Schubert","year":"2017","journal-title":"ACM Trans. Database Syst. TODS"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_20","first-page":"1038","article-title":"Non-linear Dimensionality Reduction by Locally Linear Isomaps","volume":"3316","author":"Saxena","year":"2004","journal-title":"Neural Inf. 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