Journal papers

[1] S. Chamadia and D. A. Pados, “Optimal algorithms for sparse $L_1$-principal component analysis,” IEEE Trans. Signal Proc., 2017 (submitted).

[2] P. P. Markopoulos, S. Kundu, S. Chamadia , and D. A. Pados, “Efficient $L_1$- norm principal-component analysis via bit flipping,” IEEE Trans. Signal Proc., vol. 65, pp. 4252-4264, Aug. 2017.





Conference papers

[1] S. Chamadia and D. A. Pados, “Outlier processing via $L_1$- principal subspaces,” in proc. Flor. Arti. Intell. Res. Soc. (FLAIRS), Florida, May 2017, pp. 508-513.

[2] S. Chamadia and D. A. Pados, “Optimal sparse $L_1$-norm principal component analysis,” in proc. IEEE Intern. Conf. on Acoust., Speech, and Signal Process. (ICASSP), New Orleans, Mar. 2017, pp. 2686-2690.

[3] P. P. Markopoulos, S. Kundu, S. Chamadia , and D. A. Pados, “$L_1$-norm principal-component analysis via bit flipping,” in proc. Int. Conf. Mach. Learn. Appl. (ICMLA), California, Dec. 2016, pp. 326-332.

[4] Y. Li, S. Chamadia, and D. A. Pados, “Joint-view kalman-filter recovery of compressed-sensed multiview videos,” in proc. IEEE Intern. Conf. on Acoust., Speech, and Signal Process. (ICASSP), Shanghai, Mar. 2016, pp. 1721-1725.

[5] S. Kundu, S. Chamadia, D. A. Pados, and S. N. Batalama, “Fastest-known near-ml decoding of golden codes,” in proc. IEEE Intern. Signal Process. Adv. Wireless. Commun. (SPAWC), Toronto, june 2014, pp. 209-213.





Technical posters

[1] S. Chamadia and D. A. Pados, “Optimal sparse $L_1$-norm principal component analysis,” in proc. IEEE Intern. Conf. on Acoust., Speech, and Signal Process. (ICASSP), New Orleans, Mar. 2017.

[2] S. Kundu, S. Chamadia, D. A. Pados, and S. N. Batalama, “Fastest-known near-ml decoding of golden codes,” in proc. IEEE Intern. Signal Process. Adv. Wireless. Commun. (SPAWC), Toronto, june 2014.



Technical presentations

[1] S. Chamadia “Outlier processing via $L_1$-principal subspaces,” in Florida Arti. Intell. Res. Soc. (FLAIRS), Marco Island, Florida, May 2017.

[2] S. Chamadia “Kernel PCA and near-optimal $L_1$-sparse PCA,” in University at Buffalo, New York, Mar. 2017.

[3] S. Chamadia “Living on the Fringe: Outlier removal via $L_1$-principal components,” in University at Buffalo, New York, May 2016.

[4] S. Chamadia “The Optimal $L_1$-Sparse Principal Components,” in University at Buffalo, New York, Oct. 2015.



Manuscript in prepration

[1] S. Chamadia, S. Kundu, S. and D. A. Pados, “Computational advances in sparse $L_1$-norm principal component analysis of multi-dimensional data,” in Camsap, 2017.

[2] S. Kundu, S. Chamadia, and D. A. Pados, “Hybrid-ARQ: A paradigm in communication security measure,” in IEEE Trans. Wireless Commun., 2017.



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