Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data

no code implementations • 28 Dec 2022 • Sakib Abrar, Ali Sekmen, Manar D. Samad

Eight clustering and state-of-the-art embedding clustering methods proposed for image data sets are tested on seven tabular data sets.

Clustering

Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data

no code implementations • 17 May 2022 • Manar Samad, Sakib Abrar

Unlike dropout learning, the proposed weight perturbation routine additionally achieves 15% to 40% sparsity across six tabular data sets for the compression of deep pretrained models.

BIG-bench Machine Learning Classification +2

Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework

no code implementations • 28 Feb 2022 • Manar D Samad, Sakib Abrar, Norou Diawara

Our extensive analyses involving six tabular data sets, up to 80% missingness, and three missingness types (missing completely at random, missing at random, missing not at random) reveal that ensemble or deep learning within MICE is superior to the baseline MICE (b-MICE), both of which are consistently outperformed by CISCL.

Clustering Ensemble Learning +1