ICASSP2020 three papers
5月4日から8日にバルセロナ（スペイン）で開催される信号処理分野・トップ会議 IEEE 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSPS) 2020 に3件の研究論文が採択されました．
- Sequential semi-orthogonal multi-level NMF with negative residual reduction for network embedding [paper (publisher’s site)]
- Authors: R.H. (The University of Electro-Communications) and H.Kasai
- Abstract: Network embedding is intended to produce low-dimensional vector representations of nodes in a network to preserve and extract the latent network structure, which has higher robustness to noise, outliers, and redundant data. Although a recently proposed multi-level nonnegative matrix factorization (NMF)-based approach has exhibited superior performance on network analysis, it is adversely affected by performance degradation because of discarded negative residual and redundant base selection throughout sequential multiple factorization processes. To alleviate this shortcoming, this paper presents a proposal of a sequential semi-orthogonal NMF with negative residual reduction for the network embedding (SSO-NRR-NMF). The proposed approach reduces the negative residuals to be discarded, and avoids redundant bases with a semi-orthogonal constraint. Numerical evaluations conducted using several real-world datasets demonstrate the effectiveness of the proposed SSO-NRR-NMF.
Overlapped State Hidden Semi-Markov Model for Grouped Multiple Sequences (paper (publisher’s site)]
- Authors: H.Narimatsu (The University of Electro-Communications, NTT Communication Science Laboratories) and H.Kasai
- Abstract: Efficient analysis of multiple sequential data is becoming necessary for identifying sequential patterns of multiple objects of interest. This analysis has major practical and technical importance because finding such patterns necessitates extraction and discovery of latent but meaningful groups of sequences from apparently extraneous but mutually interrelated multiple sequences. However, conventional sequential data analysis methods have not specifically examined this particular technical direction. To tackle this challenge, we propose a new model designated as overlapped state hidden semi-Markov model (OS-HSMM). The model represents the lengths of intervals and overlap among multiple events that are semantically interpretable and appearing across multiple sequences. The salient contribution is that OS-HSMM represents the overlap of two states by extending the state duration probability in HSMM to allow a negative value. Consequently, it handles the state interval and the state overlap simultaneously. Results of our evaluations underscore the effectiveness of our model.
Multi-View Wasserstein Discriminant Analysis with Entropic Regularized Wasserstein Distance [paper (publisher’s site)]
- Author: H.Kasai
- Abstract: Analysis of multi-view data has recently garnered growing attention because multi-view data frequently appear in real-world applications, which are collected or taken from many sources or captured using various sensors. A simple and popular promising approach is to learn a latent subspace shared by multi-view data. Nevertheless, because one sample lies in heterogeneous structure types, many existing multi-view data analyses show that discrepancies in within-class data across multiple views have a larger value than discrepancies within the same view from different views. To evaluate this discrepancy, this paper presents a proposal of a multi-view Wasserstein discriminant analysis, designated as MvWDA, which exploits a recently developed optimal transport theory. Numerical evaluations using three real-world datasets reveal the effectiveness of the proposed MvWDA.
Conference information: IEEE ICASSP
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