Mainali, Sambriddhi, Garzon, Max, Venugopal, Deepak, Jana, Kalidas, Yang, Ching-Chi, Kumar, Nirman, Bowman, Dale, and Deng, Lih-Yuan. An Information-theoretic approach to dimensionality reduction in data science. Retrieved from https://par.nsf.gov/biblio/10384829. International Journal of Data Science and Analytics 12.3 Web. doi:10.1007/s41060-021-00272-2.

Mainali, Sambriddhi, Garzon, Max, Venugopal, Deepak, Jana, Kalidas, Yang, Ching-Chi, Kumar, Nirman, Bowman, Dale, & Deng, Lih-Yuan. An Information-theoretic approach to dimensionality reduction in data science. International Journal of Data Science and Analytics, 12 (3). Retrieved from https://par.nsf.gov/biblio/10384829. https://doi.org/10.1007/s41060-021-00272-2

Mainali, Sambriddhi, Garzon, Max, Venugopal, Deepak, Jana, Kalidas, Yang, Ching-Chi, Kumar, Nirman, Bowman, Dale, and Deng, Lih-Yuan. "An Information-theoretic approach to dimensionality reduction in data science". International Journal of Data Science and Analytics 12 (3). Country unknown/Code not available. https://doi.org/10.1007/s41060-021-00272-2. https://par.nsf.gov/biblio/10384829.

@article{osti_10384829,
place = {Country unknown/Code not available}, title = {An Information-theoretic approach to dimensionality reduction in data science}, url = {https://par.nsf.gov/biblio/10384829}, DOI = {10.1007/s41060-021-00272-2}, abstractNote = {}, journal = {International Journal of Data Science and Analytics}, volume = {12}, number = {3}, author = {Mainali, Sambriddhi and Garzon, Max and Venugopal, Deepak and Jana, Kalidas and Yang, Ching-Chi and Kumar, Nirman and Bowman, Dale and Deng, Lih-Yuan}, }