2,725 research outputs found
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
U.S.-Mexico trade: are we still connected?
Exports ; Imports ; Maquiladora
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
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,
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