Adaptive-Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Shicheng Li,Wenle Wang,Yugen Yi,Jiangyan Dai,Qinghua Liu,Xiaolin Gui,Wei Quan

doi:10.1155/2021/5526479

Abstract

Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

Highlights

With the rapid development of computer technology, the collected multimedia data from many research fields, such as computer vision, image processing, and natural language processing, always have features with high dimension and complex structures
Principal component analysis (PCA) [6], independent components analysis (ICA) [7], vector quantization (VQ) [8], etc. are well-known matrix factorization methods that can obtain a low-rank approximation matrix by decomposing a high-dimensional data matrix, and they can effectively extract a low-dimensional representation from highdimensional data
The low-dimensional feature representations obtained by nonnegative matrix factorization (NMF) method are part-based so that they have strong interpretability

Summary

Introduction

With the rapid development of computer technology, the collected multimedia data from many research fields, such as computer vision, image processing, and natural language processing, always have features with high dimension and complex structures. Are well-known matrix factorization methods that can obtain a low-rank approximation matrix by decomposing a high-dimensional data matrix, and they can effectively extract a low-dimensional representation from highdimensional data These methods do not utilize any constraints on the matrix elements during the process of matrix decomposition. It means that the results allow negative elements, which give rise to the loss of physical meaning in low-dimensional representations To solve this problem, Lee et al added nonnegative constraints into matrix decomposition and proposed a nonnegative matrix factorization (NMF) [9] method.

Methods

Results

Conclusion