Projective complex matrix factorization for facial expression recognition

Duong, Viet Hang; Lee, Yuan Shan; Ding, Jian; Pham, Bach Tung; Bui, Manh Quan; Wang, Jia Ching

doi:10.1186/s13634-017-0521-9

Cited by 9 publications

(11 citation statements)

References 43 publications

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“…In the first step of our clustering method, overall users' interests and websites' features are selected by using nonnegative matrix factorization (NMF). NMF has been used in image processing [8], co-clustering [17], and sound analysis. NMF [16] aims to decompose a large-scale matrix into low-rank latent factor matrices with nonnegative constraints.…”

Section: Picking Average Feature Of Users and Websitesmentioning

confidence: 99%

New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests

Yoshida

Higurashi²,

Maruishi

et al. 2019

Data Sci. Eng.

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The studies of browsing behavior have gained increasing attention in web analysis for providing better service. Most of the conventional approaches focus on simple indices such as average dwell time and conversion rate. These indices make similar evaluations to websites even if their features are significantly different. Moreover, such statistical indices are not sensitive to the dynamics of users' interests. In this paper, we propose a new framework for measuring a website's attractiveness that takes into account both the distribution and dynamics of users' interests. Within the framework, we define a new index for the website, called Attractiveness Factor, which evaluates the degree of users' attention. It consists of three procedures: First, we capture the transition of users' interests during browsing by solving a nonnegative matrix factorization and constrained network flow problems. To accommodate multiple types of interests of a user, we applied a soft clustering as opposed to a hard clustering to model attributes of users and websites. Second, for each website, the feature of each cluster is obtained by fitting the dwell time distribution with Weibull distribution. Finally, we calculate Attractiveness Factor of a website by applying the results of clustering and fitting. Attractiveness Factor depends on the distribution of the dwell time of users interested in the website, which reflects the change of interest of users. Numerical experiments with real web access data of Yahoo Japan News are conducted by solving extremely large-scale optimization problems. They show that Attractiveness Factor captures more exceptional information about browsing behavior more effectively than well-used indices. Attractive factors give low ratings to category pages; however, it can assign high ratings to websites that attract many people, such as hot topic news about the 2018 FIFA World Cup, Japan's new imperial era' REIWA,' and North Korea-the United States Hanoi Summit. Moreover, we demonstrate that Attractiveness Factor can detect the tendency of users' attention to each website at a given time interval of the day.

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Section: Picking Average Feature Of Users and Websitesmentioning

confidence: 99%

New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests

Yoshida

Higurashi²,

Maruishi

et al. 2019

Data Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Traditional methods such as canonical k-means clustering on the original data space (KM) [19], graph regularized NMF (GNMF) [3], a weighted NMF with different error weights for different entries (weNMF) [20], nonnegative graph embedding (NGE) [6], complex matrix factorization (CMF) [9], and exemplar-embed complex matrix factorization (EE-CMF) [10] are compared.…”

Section: Methodsmentioning

confidence: 99%

“…Theoretically, α represents the frequency of the cosine function and is optimized to suppress the values caused by outliers [9]. However, this parameter is not critical as we can see from CMF [9], EE-CMF [10], and siCMF, which simply set α to 1 and still had competitive performance. To fasten computation, we set α = 1.…”

Section: Algorithm 2: Algorithm For Image Clusteringmentioning

confidence: 99%

“…Through this transformation space, the optimization problem using cosine dissimilarity in the real field will be replaced by an equivalent complex optimization problem where the Frobenious norm is employed for reconstruction error measurement [10], [11].…”

Section: Fast Robust Correlation and Image Representation In Complex mentioning

confidence: 99%

“…Motivated by the interesting works of Liwicki et al [9] and Duong et al [10], [11], which showed the effectiveness of projecting pixel intensity values to the complex plane, this work develops a new approach for data representation, named simplicial complex matrix factorization (siCMF). Herein, the cosine dissimilarity, used on the reconstruction error function, is exploited judiciously by transferring data from the real-valued space to the complex field.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Approach of Matrix Factorization on Complex Domain for Data Representation

Duong

Bui

Ding

et al. 2017

IEICE Trans. Inf. & Syst.

Self Cite

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SUMMARYThis work presents a new approach which derives a learned data representation method through matrix factorization on the complex domain. In particular, we introduce an encoding matrix-a new representation of data-that satisfies the simplicial constraint of the projective basis matrix on the field of complex numbers. A complex optimization framework is provided. It employs the gradient descent method and computes the derivative of the cost function based on Wirtinger's calculus.

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Facial expression recognition techniques: a comprehensive survey

Rajan¹,

Chenniappan²,

Devaraj

et al. 2019

IET Image Processing

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Over the past decades, facial expression recognition (FER) has become an interesting research area and achieved substantial progress in computer vision. FER is to detect human emotional state related to biometric traits. Developing a machine based human FER system is a quite challenging task. Various FER systems are developed by analysing facial muscle motion and skin deformation based algorithms. In conventional FER system, the developed algorithms work on the constrained database. In the unconstrained environment, the efficacy of existing algorithms is limited due to certain issues during image acquisition. This study presents a detailed study on FER techniques, classifiers and datasets used for analysing the efficacy of the recognition techniques. Moreover, this survey will assist researchers in understanding the strategies and innovative methods that address the issues in a real-time application. Finally, the review presents the challenges encountered by FER system along with the future direction.

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Projective complex matrix factorization for facial expression recognition

Cited by 9 publications

References 43 publications

New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests

New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests

A New Approach of Matrix Factorization on Complex Domain for Data Representation

Facial expression recognition techniques: a comprehensive survey

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