Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/8744
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVikram, M.
dc.contributor.authorPavan, R.
dc.contributor.authorDineshbhai, N.D.
dc.contributor.authorMohan, B.
dc.date.accessioned2020-03-30T10:22:39Z-
dc.date.available2020-03-30T10:22:39Z-
dc.date.issued2019
dc.identifier.citationProceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019, 2019, Vol.2019-April, , pp.1169-1174en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8744-
dc.description.abstractWith a large amount of data being generated each day, the task on analyzing and making inferences from data is becoming an increasingly challenging task. One of the major challenges is the curse of dimensionality which is dealt with by using several popular dimensionality reduction techniques such as ICA, PCA, NMF etc. In this work, we make a systematic performance evaluation of the efficiency and effectiveness of various dimensionality reduction techniques. We present a rigorous evaluation of various techniques benchmarked on real-world datasets. This work is intended to assist data science practitioners to select the most suitable dimensionality reduction technique based on the trade-off between the corresponding effectiveness and efficiency. �2019 IEEE.en_US
dc.titlePerformance evaluation of dimensionality reduction techniques on high dimensional dataen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.