Learning Probability Measures with respect to Optimal Transport Metrics

We study the problem of estimating, in the sense of optimal transport metrics, a measure which is assumed supported on a manifold embedded in a Hilbert space. By establishing a precise connection between optimal transport metrics, optimal quantization, and learning theory, we derive new probabilistic bounds for the performance of a classic algorithm in unsupervised learning (k-means), when used to produce a probability measure derived from the data. In the course of the analysis, we arrive at new lower bounds, as well as probabilistic upper bounds on the convergence rate of the empirical law of large numbers, which, unlike existing bounds, are applicable to a wide class of measures.Comment: 13 pages, 2 figures. Advances in Neural Information Processing Systems, NIPS 201

Spotlight: maquiladora employment: new data confirm pickup in Juarez factory jobs

For decades, Mexico's maquiladoras have been a major growth engine in the Rio Grande region, and monthly reports on the industry's employment, wages and production were key barometers for the border region's economy. ; We developed a model to estimate Juarez's monthly maquiladora employment. This model will continue to be a timely indicator of El Paso-Juárez area manufacturing activity, given its track record and Mexico's two-month lag in reporting IMMEX (Maquiladora Manufacturing Industry and Export Services), data.Maquiladora ; Mexico ; Employment ; Economic indicators ; Mexican-American Border Region - Economic conditions

U.S.-Mexico trade: are we still connected?

Exports ; Imports ; Maquiladora

Spotlight: remittances to Mexico: cross-border money flows slowed by U.S. slump

Mexicans living in the U.S. are sending less money home. In 2009, remittances to Mexico totaled $21.5 billion, a 15 percent decline from 2008. With the exception of October 2008, remittances have been decreasing since mid-2007.Mexico ; Emigrant remittances ; Economic conditions - United States

On the Sample Complexity of Subspace Learning

A large number of algorithms in machine learning, from principal component analysis (PCA), and its non-linear (kernel) extensions, to more recent spectral embedding and support estimation methods, rely on estimating a linear subspace from samples. In this paper we introduce a general formulation of this problem and derive novel learning error estimates. Our results rely on natural assumptions on the spectral properties of the covariance operator associated to the data distribu- tion, and hold for a wide class of metrics between subspaces. As special cases, we discuss sharp error estimates for the reconstruction properties of PCA and spectral support estimation. Key to our analysis is an operator theoretic approach that has broad applicability to spectral learning methods.Comment: Extendend Version of conference pape

Landscape Assessment via Regression Analysis

This paper presents a methodology for assessing the visual quality of agricultural landscapes through direct and indirect techniques of landscape valuation. The first technique enables us to rank agricultural landscapes on the basis of a survey of public preferences. The latter weighs the contribution of the elements and attributes contained in the picture to its overall scenic beauty via regression analysis. The photos used in the survey included man-made elements, positive and negative, agricultural fields, mainly of cereals and olive trees, and a natural park. The results show that perceived visual quality increases, in decreasing order of importance, with the degree of wilderness of the landscape, the presence of well-preserved man-made elements, the percentage of plant cover, the amount of water, the presence of mountains and the colour contrast.landscape assessment, visual quality, landscape elements, landscape value, Land Economics/Use, H41, Q21, Q26,