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In this paper, we propose a supervised subspace learning method that exploits the rich representation power of deep feedforward networks. In order to derive a fast, yet efficient, learning scheme we employ deep randomized neural networks that have been recently shown to provide good compromise between training speed and performance.

For ICASSP 2016 paper, "Fast Voxel Line Update for Time-Space Image Reconstruction"

Coupled decompositions of multiple tensors are fundamental tools for multi-set data fusion. In this paper, we introduce a coupled version of the rank-(Lm, Ln, ·) block term decomposition (BTD), applicable to joint independent
subspace analysis. We propose two algorithms for its computation based on a coupled block simultaneous generalized Schur decomposition scheme. Numerical results are given to show the performance of the proposed algorithms.

The problem of estimating sparse eigenvectors of a symmetric matrix attracts a lot of attention in many applications, especially those with high dimensional data set. While classical eigenvectors can be obtained as the solution of a maximization

In this paper, we address the issue of designing a smoke detector robust to illumination variations. Our contribution consists in resorting to color invariants as salient smoke features. More precisely, the proposed detector employs consecutively of an illumination invariant color representation, a photometric gain based background subtraction, a chrominance detection and a smoke identification based on two invariant color descriptors.