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In this paper, we show that the lexical function model for composition of distributional semantic vectors can be improved by adopting a more advanced regression technique. We use the pathwise coordinate-descent optimized elastic-net regression method to estimate the composition parameters, and compare the resulting model with several recent alternative(More)
Hyperspectral sensors collect multichannel contiguous narrow spectral band imagery, spanning from the visible to the infrared portion of the electromagnetic spectrum [1]. Due to the difficulty of constructing a standard spectral library and the unsatisfactoriness of spectral matching techniques, classification of hyperspectral images is usually addressed in(More)
Classification is an important task in Hyperspectral data analysis. Hyperspectral images show strong correlations across spatial and spectral neighbors. Theoretically, classifier designed with a joint spectral and spatial correlations can improve classification performance than classifier which only utilize one of the correlations. Gaussian Processes(GPs)(More)
Hyperspectral imagery generally contains enormous amounts of data due to hundreds of spectral bands. Classification for these high-dimensional data often requires a large set of training samples and enormous processing time. Therefore, dimension reduction methods for hyperspectral data are catching the attention of researchers lately. In this paper, a(More)
We propose a simplified model which exhibits community structure, power-law degree distribution and high clustering. Every vertex is a social one with a social identity. The preferential attachment of Barabási-Albert model is incorporated with social similarity. When a newly added vertex makes a new link, it first selects a certain group of vertices(More)
Hyperspectral imagery generally contains enormous amounts of data due to hundreds of spectral bands. As recent researchers have discovered, many of the bands are highly correlated and may provide redundant information for the classification related problems. Therefore, feature selection is very important in hyperspectral image processing problem. ''Pathwise(More)
In this paper, we not only extend the temporal hierarchical alternating least squares (HALS) to spatial domain, but also incorporate two necessary characteristics of material abundances, full additivity and sparsity, to unmix hyperspectral data. The new algorithm is abbreviated as HALSSC (HALS with Sparsity Constraint). Different from the other endmember(More)
In the modern society, each one is highly affected by the Internet from fundamentally living necessities to scientific developing. Surfing the Internet has become part of our life. Based on a database recording the timing of the Internet accessing for an educational institution, we present the distributions of the time intervals in Internet surfing and(More)
Firstly we make a pretreatment to the original gene data, and analyze the information of sample gene graph by two steps. First step is removing the unrelated genes; Second step is use an extraction algorithm of label gene based on bipartite network structure to handle the candidate gene, and get gene interactive relationship network. Finally extracts some(More)
  • Jiming Li
  • 2015 7th Workshop on Hyperspectral Image and…
  • 2015
Active learning can effectively reduce labelling effort for remote sensing image classification. In this paper, we propose a new active learning method for hyperspectral image classification. We consider batch mode active learning and relatively large amount of data which can be a problem when using current state of the art algorithm based on kernel(More)