Kernel Probabilistic Dependent-Independent Canonical Correlation Analysis

被引:0
|
作者
Rohani Sarvestani, Reza [1 ]
Gholami, Ali [2 ]
Boostani, Reza [3 ]
机构
[1] Shahrekord Univ, Dept Comp Engn, Shahrekord, Iran
[2] Islamic Azad Univ, Fac Technol & Engn, Dept Elect Engn, Tehran Branch, Tehran, Iran
[3] Shiraz Univ, ECE Fac, CSE & IT Dept, Shiraz, Iran
关键词
RECOGNITION; FUSION;
D O I
10.1155/2024/7393431
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
There is growing interest in developing linear/nonlinear feature fusion methods that fuse the elicited features from two different sources of information for achieving a higher recognition rate. In this regard, canonical correlation analysis (CCA), cross-modal factor analysis, and probabilistic CCA (PCCA) have been introduced to better deal with data variability and uncertainty. In our previous research, we formerly developed the kernel version of PCCA (KPCCA) to capture both nonlinear and probabilistic relation between the features of two different source signals. However, KPCCA is only able to estimate latent variables, which are statistically correlated between the features of two independent modalities. To overcome this drawback, we propose a kernel version of the probabilistic dependent-independent CCA (PDICCA) method to capture the nonlinear relation between both dependent and independent latent variables. We have compared the proposed method to PDICCA, CCA, KCCA, cross-modal factor analysis (CFA), and kernel CFA methods over the eNTERFACE and RML datasets for audio-visual emotion recognition and the M2VTS dataset for audio-visual speech recognition. Empirical results on the three datasets indicate the superiority of both the PDICCA and Kernel PDICCA methods to their counterparts.
引用
收藏
页数:20
相关论文
共 50 条
  • [21] Discriminative Feature Extraction by a Neural Implementation of Canonical Correlation Analysis
    Sakar, Cemal Okan
    Kursun, Olcay
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (01) : 164 - 176
  • [22] Semi-supervised and semi-paired graph regularized multiset canonical correlation analysis
    Guo, Xin
    Qi, Lin
    Guan, Ling
    PROCEEDINGS OF 2016 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2016, : 379 - 384
  • [23] Local Canonical Correlation Analysis for Nonlinear Common Variables Discovery
    Yair, Or
    Talmon, Ronen
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (05) : 1101 - 1115
  • [24] Approximate Canonical Correlation Analysis for common/specific subspace decompositions
    Ranta, Radu
    Le Cam, Steven
    Chaudet, Baptiste
    Tyvaert, Louise
    Maillard, Louis
    Colnat-Coulbois, Sophie
    Louis-Dorr, Valerie
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 68
  • [25] A novel semi-supervised canonical correlation analysis and extensions for multi-view dimensionality reduction
    Shen, XiaoBo
    Sun, QuanSen
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2014, 25 (08) : 1894 - 1904
  • [26] Canonical principal angles correlation analysis for two-view data
    Wang, Sheng
    Lu, Jianfeng
    Gu, Xingjian
    Shen, Chunhua
    Xia, Rui
    Yang, Jingyu
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2016, 35 : 209 - 219
  • [27] Color image canonical correlation analysis for face feature extraction and recognition
    Jing, Xiaoyuan
    Li, Sheng
    Lan, Chao
    Zhang, David
    Yang, Jingyu
    Liu, Qian
    SIGNAL PROCESSING, 2011, 91 (08) : 2132 - 2140
  • [28] Human Action Recognition Using Hybrid Centroid Canonical Correlation Analysis
    El Madany, Nour El Din
    He, Yifeng
    Guan, Ling
    2015 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2015, : 205 - 210
  • [29] Improving SSVEP Identification Accuracy via Generalized Canonical Correlation Analysis
    Sun, Qiang
    Chen, Minyou
    Zhang, Li
    Yuan, Xiaoyang
    Li, Changsheng
    2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2021, : 61 - 64
  • [30] Canonical Correlation Analysis With Low-Rank Learning for Image Representation
    Lu, Yuwu
    Wang, Wenjing
    Zeng, Biqing
    Lai, Zhihui
    Shen, Linlin
    Li, Xuelong
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 31 : 7048 - 7062