Statistical models and learning algorithms for ordinal regression problems

Kanamori, Takafumi

doi:10.1016/j.inffus.2012.05.006

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…To account for ordinal structure in labels y (i.e., the intensity levels of AUs), different models for ordinal responses can be employed (e.g., see [33]). We adopt the latent variable approach introduced in [14].…”

Section: Ordinal Regressionmentioning

confidence: 99%

“…In both methods, we used the RBF kernel, as used in the original works [21], [20]. The width of the RBF kernel was set as the median of the (feature's) distance set, i.e., { x i − x j , i, j = 1, ..., N, i < j} [33]. The hyper/regularization-parameters of all methods were selected by a 5-fold cross validation on the training set using a grid-search in a range ρ = 10 −4 , 10 −3 , ..., 1, 2, 5 .…”

Section: Modelsmentioning

confidence: 99%

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Context-Sensitive Dynamic Ordinal Regression for Intensity Estimation of Facial Action Units

Rudovic

Pavlović

Pantić

2015

IEEE Trans. Pattern Anal. Mach. Intell.

105

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Abstract-Modeling intensity of facial action units from spontaneously displayed facial expressions is challenging mainly because of high variability in subject-specific facial expressiveness, head-movements, illumination changes, etc. These factors make the target problem highly context-sensitive. However, existing methods usually ignore this context-sensitivity of the target problem. We propose a novel Conditional Ordinal Random Field (CORF) model for context-sensitive modeling of the facial action unit intensity, where the W5+ (who, when, what, where, why and how) definition of the context is used. While the proposed model is general enough to handle all six context questions, in this paper we focus on the context questions: who (the observed subject), how (the changes in facial expressions), and when (the timing of facial expressions and their intensity). The context questions who and how are modeled by means of the newly introduced context-dependent covariate effects, and the context question when is modeled in terms of temporal correlation between the ordinal outputs, i.e., intensity levels of action units. We also introduce a weighted softmax-margin learning of CRFs from data with skewed distribution of the intensity levels, which is commonly encountered in spontaneous facial data. The proposed model is evaluated on intensity estimation of pain and facial action units using two recently published datasets (UNBC Shoulder Pain and DISFA) of spontaneously displayed facial expressions. Our experiments show that the proposed model performs significantly better on the target tasks compared to the state-of-the-art approaches. Furthermore, compared to traditional learning of CRFs, we show that the proposed weighted learning results in more robust parameter estimation from the imbalanced intensity data.

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Section: Ordinal Regressionmentioning

confidence: 99%