Joshua M. Lewis

Multi-view kernel construction

In many problem domains data may come from multiple sources (or views), such as video and audio from a camera or text on and links to a web page. These multiple views of the data are often not directly comparable to one another, and thus a principled method for their integration is warranted. In this paper we develop a new algorithm to leverage information from multiple views for unsupervised clustering by constructing a custom kernel. We generate a multipartite graph (with the number of parts given by the number of views) that induces a kernel we then use for spectral clustering. Our algorithm can be seen as a generalization of co-clustering and spectral clustering and a relative of Kernel Canonical Correlation Analysis. We demonstrate the algorithm on four data sets: an illustrative artificial data set, synthetic fMRI data, voxels from an fMRI study, and a collection of web pages. Finally, we compare its performance to common alternatives.

A Unified Account of Gaze Following

Gaze following, the ability to redirect ones visual attention to look at what another person is seeing, is foundational for imitation, word learning, and theory-of-mind. Previous theories have suggested that the development of gaze following in human infants is the product of a basic gaze following mechanism, plus the gradual incorporation of several distinct new mechanisms that improve the skill, such as spatial inference, and the ability to use eye direction information as well as head direction. In this paper, we offer an alternative explanation based on a single learning mechanism. From a starting state with no knowledge of the implications of another organisms gaze direction, our model learns to follow gaze by being placed in a simulated environment where an adult caregiver looks around at objects. Our infant model matches the development of gaze following in human infants as measured in key experiments that we replicate and analyze in detail.

Mapping Uncharted Waters: Exploratory Analysis, Visualization, and Clustering of Oceanographic Data

In this paper we describe an interdisciplinary collaboration between researchers in machine learning and oceanography. The collaboration was formed to study the problem of open ocean biome classification. Biomes are regions on Earth with similar climate (e.g., temperature and rainfall) and vegetation structure (e.g., grasslands, coniferous forests, and deserts). To discover biomes in the open ocean, we apply leading methods in high dimensional data analysis, clustering, and visualization to oceanographic measurements culled from multiple existing databases. We compare traditional approaches, such as k-means clustering and principal component analysis, to newer approaches such as Isomap and maximum variance unfolding. Our work provides the first quantitative classification of open ocean biomes from an automated statistical analysis of multivariate data. It also provides a valuable case study in the use (and misuse) of recently developed algorithms for high dimensional data analysis.