核概率依赖相关分析

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Intelligent Systems Pub Date : 2024-01-03 DOI:10.1155/2024/7393431
Reza Rohani Sarvestani, Ali Gholami, Reza Boostani
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引用次数: 0

摘要

人们对开发线性/非线性特征融合方法的兴趣与日俱增,这种方法可以融合来自两个不同信息源的特征,以实现更高的识别率。在这方面,为了更好地处理数据的可变性和不确定性,人们引入了典型相关分析(CCA)、跨模态因子分析和概率CCA(PCCA)。在之前的研究中,我们开发了核版本的 PCCA(KPCCA),以捕捉两个不同源信号特征之间的非线性和概率关系。然而,KPCCA 只能估计两个独立模态特征之间存在统计相关性的潜变量。为了克服这一缺点,我们提出了一种核版本的概率依赖独立 CCA(PDICCA)方法,以捕捉依赖潜变量和独立潜变量之间的非线性关系。我们在用于视听情感识别的 eNTERFACE 和 RML 数据集以及用于视听语音识别的 M2VTS 数据集上比较了 PDICCA、CCA、KCCA、跨模态因子分析(CFA)和核 CFA 方法。对这三个数据集的实证结果表明,PDICCA 和核 PDICCA 方法优于同类方法。
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Kernel Probabilistic Dependent-Independent Canonical Correlation Analysis

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.

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来源期刊
International Journal of Intelligent Systems
International Journal of Intelligent Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
14.30%
发文量
304
审稿时长
9 months
期刊介绍: The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.
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