Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.

Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression

Abstract: Facial aging and facial rejuvenation analyze a given face photograph to predict a future look or estimate a past look of the person. To achieve this, it is critical to preserve human identity and the corresponding aging progression and regression with high accuracy. However, existing methods cannot simultaneously handle these two objectives well. We propose a novel generative adversarial network based approach, named the Conditional Multi-Adversarial AutoEncoder with Ordinal Regression (CMAAE-OR). It utilizes an age estimation technique to control the aging accuracy and takes a high-level feature representation to preserve personalized identity. Specifically, the face is first mapped to a latent vector through a convolutional encoder. The latent vector is then projected onto the face manifold conditional on the age through a deconvolutional generator. The latent vector preserves personalized face features and the age controls facial aging and rejuvenation. A discriminator and an ordinal regression are imposed on the encoder and the generator in tandem, making the generated face images to be more photorealistic while simultaneously exhibiting desirable aging effects. Besides, a high-level feature representation is utilized to preserve personalized identity of the generated face. Experiments on two benchmark datasets demonstrate appealing performance of the proposed method over the state-of-the-art.

Binary and Ordinal Data Analysis in Economics: Modeling and Estimation

Abstract: This chapter is concerned with the analysis of statistical models for binary and ordinal outcomes Binary data arise when a particular response variable of interest yi can take only two values, ie yi ∈ {0, 1}, where the index i = 1, , n refers to units in the sample such as individuals, families, firms, and so on Such dichotomous outcomes are widespread in the social and natural sciences For example, to understand socio-economic processes, economists often need to analyze individuals’ binary decisions such as whether to make a particular purchase, participate in the labor force, obtain a college degree, see a doctor, migrate to a different country, or vote in an election By convention, yi = 1 typically indicates the occurrence of the event of interest, whereas the occurrence of its complement is denoted by yi = 0

Indices of ordinal variation

Abstract: Properties of the index of ordinal variation (IOV) and Leik's ordinal variation (LOV) measure are examined. The two measures for ordinal variation are compared using numerical examples.