The Statistical Utilization of Multiple Measurements

Fisher, R. A.

doi:10.1111/j.1469-1809.1938.tb02189.x

Cited by 720 publications

(353 citation statements)

References 7 publications

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“…Two classical problems with the definition of these metrics are the selection of an appropriate pdf that can estimate the true underlying density of the data, and the homoscedasticity (i.e., same variance) assumption. For instance, if every class is defined by a single multimodal Normal distribution with common covariance matrix, then the nearest-mean classifier provides the Bayes optimal classification boundary in the subspace defined by linear discriminant analysis (LDA) (33).…”

Section: Significancementioning

confidence: 99%

Compound facial expressions of emotion

Tao

Martı́nez

2014

Proc. Natl. Acad. Sci. U.S.A.

658

467

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Understanding the different categories of facial expressions of emotion regularly used by us is essential to gain insights into human cognition and affect as well as for the design of computational models and perceptual interfaces. Past research on facial expressions of emotion has focused on the study of six basic categories-happiness, surprise, anger, sadness, fear, and disgust. However, many more facial expressions of emotion exist and are used regularly by humans. This paper describes an important group of expressions, which we call compound emotion categories. Compound emotions are those that can be constructed by combining basic component categories to create new ones. For instance, happily surprised and angrily surprised are two distinct compound emotion categories. The present work defines 21 distinct emotion categories. Sample images of their facial expressions were collected from 230 human subjects. A Facial Action Coding System analysis shows the production of these 21 categories is different but consistent with the subordinate categories they represent (e.g., a happily surprised expression combines muscle movements observed in happiness and surprised). We show that these differences are sufficient to distinguish between the 21 defined categories. We then use a computational model of face perception to demonstrate that most of these categories are also visually discriminable from one another.

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Section: Significancementioning

confidence: 99%

Compound facial expressions of emotion

Tao

Martı́nez

2014

Proc. Natl. Acad. Sci. U.S.A.

658

467

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“…LDA selects those basis vectors that maximize the distance between the means of each class and minimize the distance between the samples in each class and their corresponding class means [8]. This can facilitate the task of feature extraction in some applications.…”

Section: Learning Linear Subspace Representationsmentioning

confidence: 99%

Recognition of expression variant faces using weighted subspaces

Zhang¹,

Martinez²

2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

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“…A raw version of this data set can be found in our web page, http://www.imse.lsu.edu/vangelis. We analyzed these data by using Fisher's linear discriminant analysis (29)(30)(31). By using linear discriminant analysis (LDA) one can estimate the line that minimizes the misclassification probability (given that this linear combination of the features follow a normal distribution and the classes have the same variance-covariance matrix).…”

Section: Figmentioning

confidence: 99%

The Reliability Issue of Computer-Aided Breast Cancer Diagnosis

Kovalerchuk

Triantaphyllou

Ruiz

et al. 2000

Computers and Biomedical Research

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This paper introduces a number of reliability criteria for computer-aided diagnostic systems for breast cancer. These criteria are then used to analyze some published neural network systems. It is also shown that the property of monotonicity for the data is rather natural in this medical domain, and it has the potential to significantly improve the reliability of breast cancer diagnosis while maintaining a general representation power. A central part of this paper is devoted to the representation/narrow vicinity hypothesis, upon which existing computer-aided diagnostic methods heavily rely. The paper also develops a framework for determining the validity of this hypothesis. The same framework can be used to construct a diagnostic procedure with improved reliability. ᭧

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The Statistical Utilization of Multiple Measurements

Cited by 720 publications

References 7 publications

Compound facial expressions of emotion

Compound facial expressions of emotion

Recognition of expression variant faces using weighted subspaces

The Reliability Issue of Computer-Aided Breast Cancer Diagnosis

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