original LPP SRLP LLE SRE Figure 1: SwissRoll 2D projections form 3D We adapt the Locality Preserving Projections (LPP)[4] and Locally Linear Embedding (LLE)[5] techniques to preserve the sparse representation property in the embedded space. The resulting techniques are Sparse Representation based Linear Projections (SRLP) and Sparse Representation based Embedding (SRE). We compare them to the ...

Many signal processing and machine learning algorithms perform poorly when applied to high-dimensional data, as is known by the phenomenon of the curse of dimensionality. Learning low-dimensional representations aims at reducing the dimensionality of the observation space while maintaining the characteristics of the data. Further, lowdimensional representations can help to reveal latent structu...

in land use planning, mapping the present land use / land cover situation is a necessary tool for determining the current condition and for identifying land use trends. in this study, in order to provide a land use/ land cover map for ilam watershed, the irs-1c image data from 25th april 2006 were used. initial qualitative evaluation on data showed no significant radiometric error. ortho-rectif...

In land use planning, mapping the present land use / land cover situation is a necessary tool for determining the current condition and for identifying land use trends. In this study, in order to provide a land use/ land cover map for Ilam watershed, the IRS-1C image data from 25th April 2006 were used. Initial qualitative evaluation on data showed no significant radiometric error. Ortho-rectif...

The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Object recognition has been effectively performed by aggregating sparse codes of local features in an image at multiple spatial scales. Though sparse coding guarantees a highfidelity representation, it does not exploit the dependence between the local features. By incorpor...