Abstract
In this letter, we investigate the problem of classification with high-dimensional data using low-dimensional random projections in the presence of inter-and intra-signal correlations. Each sensor is assumed to compress its high-dimensional (Gaussian) signal vector using random projections in a multisensor setting. In order to quantify the classification performance with compressed data, we consider the Bhattacharya distance as the performance metric. In the presence of intra-signal correlation at a given sensor, the degradation in the Bhattacharya distance with compressed data is shown to be nonlinear with the compression ratio in contrast to the case when there is no intra-signal correlation. In the presence of inter-signal correlation, the degradation in the Bhattacharya distance with compressed data depends on whether or not an identical projection matrix is used to compress data at multiple sensors.
Original language | English (US) |
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Article number | 8421014 |
Pages (from-to) | 1398-1402 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 25 |
Issue number | 9 |
DOIs | |
State | Published - Sep 2018 |
Keywords
- Bhattacharya distance
- classification
- compressive sensing
- correlated data
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics