Deep Probabilistic NMF Using Denoising Autoencoders - Volume 8 Number 1 (Feb. 2018) - IJMLC
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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(1): 49-53 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.1.662

Deep Probabilistic NMF Using Denoising Autoencoders

Satwik Bhattamishra
Abstract—Non Negative Matrix Factorization (NMF) has received considerable attention due to its application in pattern recognition and computer vision. However, the algorithm is sensitive to noise and assumes that the signals in the data can be linearly reconstructed. In this paper, we propose a robust non-linear probabilistic model and develop its optimization algorithm. The proposed model reduces the data to a lower dimensional manifold to get a more meaningful representation and takes into account the noisy nature of the data to improve the clustering performance of NMF. Additionally, our empirical study validates the effectiveness of the proposed method on some benchmark datasets.

Index Terms—Clustering, denoising autoencoders, dimension reduction, non-negative matrix factorization.

Satwik Bhattamishra is with Birla Institute of Technology and Science Pilani, Pilani Campus, India (e-mail: satwik55@gmail.com).

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Cite: Satwik Bhattamishra, "Deep Probabilistic NMF Using Denoising Autoencoders," International Journal of Machine Learning and Computing vol. 8, no. 1, pp. 49-53, 2018.

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