
CPPCA—CompressiveProjection Principal Component Analysis
James E. Fowler
About CPPCA 

Principal component analysis (PCA) is often
central to dimensionality reduction and compression
in many applications,
yet its datadependent nature as a transform
computed via expensive eigendecomposition often
hinders its use in severely resourceconstrained settings
such as satelliteborne sensors.
Compressiveprojection principal component analysis (CPPCA)
is a process that effectively shifts the
computational burden of PCA from the resourceconstrained encoder to
a presumably more capable basestation decoder.
CPPCA is driven by projections at the sensor
onto lowerdimensional subspaces chosen at random, while the CPPCA decoder,
given only these random projections,
recovers not only the coefficients associated with the PCA transform,
but also an approximation to the PCA transform basis itself.
The reconstruction process at the CPPCA decoder consists of a novel
eigenvector reconstruction based on a
convexset optimization driven by Ritz vectors within
the projected subspaces.
Publications 


J. E. Fowler,
“CompressiveProjection Principal Component Analysis and the First Eigenvector,”
in Proceedings of the IEEE Data Compression Conference,
J. A. Storer and M. W. Marcellin, Eds.,
Snowbird, UT, March 2009, pp. 223232.

J. E. Fowler,
“CompressiveProjection Principal Component Analysis for the Compression of Hyperspectral Signatures,”
in Proceedings of the IEEE Data Compression Conference,
J. A. Storer and M. W. Marcellin, Eds.,
Snowbird, UT, March 2008, pp. 8392.

J. E. Fowler,
“CompressiveProjection Principal Component Analysis,”
IEEE Transactions on Image Processing, vol. 18, pp. 22302242, October 2009.

J. E. Fowler, Q. Du, W. Zhu, and N. H. Younan,
“Classification Performance of RandomProjectionBased Dimensionality Reduction of Hyperspectral Imagery,”
in Proceedings of the International Geoscience
and Remote Sensing Symposium, Capetown, South Africa,
July 2009, vol. 5, pp. 7679.

W. Li and J. E. Fowler,
“DecoderSide Dimensionality Determination for CompressiveProjection Principal Component Analysis of Hyperspectral Data,”
in Proceedings of the International Conference on Image Processing,
Brussels, Belgium, September 2011,
pp. 329332.

J. E. Fowler and Q. Du,
“Reconstructions from Compressive Random Projections of Hyperspectral Imagery,”
in Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques,
S. Prasad, L. M. Bruce, and J. Chanussot, Eds.
Springer, 2011, ch. 3, pp. 3148.

J. E. Fowler and Q. Du,
“Anomaly Detection and Reconstruction from Random Projections,”
IEEE Transactions on Image Processing,
vol. 21, no. 1, pp. 184195, January 2012.

W. Li, S. Prasad, and J. E. Fowler,
“Classification and Reconstruction from Random Projections for Hyperspectral Imagery,”
IEEE Transactions on Geoscience and Remote Sensing,
vol. 51, no. 2, pp. 833843, February 2013.

N. H. Ly, Q. Du, and J. E. Fowler,
“Reconstruction from Random Projections of Hyperspectral Imagery with Spectral and Spatial Partitioning,”
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
vol. 6, no. 2, pp. 466472, April 2013.

W. Li, S. Prasad, and J. E. Fowler,
“Integration of SpectralSpatial Information for
Hyperspectral Image Reconstruction from
Compressive Random Projections,”
IEEE Geoscience and Remote Sensing Letters,
vol. 10, no. 6, pp. 13791383, November 2013.

C. Chen, W. Li, E. W. Tramel, and J. E. Fowler,
“Reconstruction of Hyperspectral Imagery from Random Projections Using Multihypothesis Prediction,”
IEEE Transactions on Geoscience and Remote Sensing,
vol. 52, no. 1, pp. 365374, January 2014.
Software 

Support 

This material is based upon work supported by the National Science Foundation under Grant No. 0915307.
Any opinions, findings and conclusions, or recomendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation (NSF).
Last update: 30jun2023
