Collaborative Multi-sensor Classification via Sparsity-based Representation

10/29/2014
by   Minh Dao, et al.
0

In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations. We also robustify our models to deal with the presence of sparse noise and low-rank interference signals. Specifically, we demonstrate that incorporating the noise or interference signal as a low-rank component in our models is essential in a multi-sensor classification problem when multiple co-located sources/sensors simultaneously record the same physical event. We further extend our frameworks to kernelized models which rely on sparsely representing a test sample in terms of all the training samples in a feature space induced by a kernel function. A fast and efficient algorithm based on alternative direction method is proposed where its convergence to an optimal solution is guaranteed. Extensive experiments are conducted on several real multi-sensor data sets and results are compared with the conventional classifiers to verify the effectiveness of the proposed methods.

READ FULL TEXT
research
12/17/2019

Collaborative representation-based robust face recognition by discriminative low-rank representation

We consider the problem of robust face recognition in which both the tra...
research
07/21/2023

Data-Induced Interactions of Sparse Sensors

Large-dimensional empirical data in science and engineering frequently h...
research
04/18/2014

Robust Face Recognition via Adaptive Sparse Representation

Sparse Representation (or coding) based Classification (SRC) has gained ...
research
06/03/2019

HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization

Partial Label Learning (PLL) aims to learn from the data where each trai...
research
02/06/2019

Robust One-Class Kernel Spectral Regression

The kernel null-space technique and its regression-based formulation (ca...
research
10/07/2014

Hierarchical Sparse and Collaborative Low-Rank Representation for Emotion Recognition

In this paper, we design a Collaborative-Hierarchical Sparse and Low-Ran...
research
01/16/2014

Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

Pixel-wise classification, where each pixel is assigned to a predefined ...

Please sign up or login with your details

Forgot password? Click here to reset