Andrew J. Landgraf

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Principal component analysis (PCA) for binary data, known as logistic PCA, has become a popular alternative to dimensionality reduction of binary data. It is motivated as an extension of ordinary PCA by means of a matrix factorization, akin to the singular value decomposition, that maximizes the Bernoulli log-likelihood. We propose a new formulation of(More)
Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients' own paralyzed limbs. To date, human studies have demonstrated an "all-or-none" type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use,(More)
Principal component analysis (PCA) is very useful for a wide variety of data analysis tasks, but its implicit connection to the Gaussian distribution can be undesirable for discrete data such as binary and multi-category responses or counts. We generalize PCA to handle various types of data using the generalized linear model framework. In contrast to the(More)
INTRODUCTION Evidence regarding impact of community policies and programs (CPPs) to prevent child obesity is limited, and which combinations of strategies and components are most important is not understood. The Healthy Communities Study was an observational study to assess relationships of characteristics and intensity of CPPs with adiposity, diet, and(More)
Mikolov et al. (2013) introduced the skip-gram formulation for neural word embeddings, wherein one tries to predict the context of a given word. Their negative-sampling algorithm improved the computational feasibility of training the embeddings. Due to their state-of-the-art performance on a number of tasks, there has been much research aimed at better(More)
NEXTRANS Project No. 125OSUY2.1 VARIATIONAL BAYES METHOD FOR ESTIMATING TRANSIT ROUTE OD FLOWS USING APC DATA By Rabi G. Mishalani, Principal Investigator Professor of Civil, Environmental, and Geodetic Engineering The Ohio State University Mark R. McCord, co-Principal Investigator Professor of Civil, Environmental, and Geodetic(More)