From Event: SPIE Optical Engineering + Applications, 2017
We present a computationally efficient, data-driven procedure for constructing linear isometric embeddings of high-dimensional data (data frames) into spaces of smooth images, and thereby obtain tight frame dictionaries for the data space using tight frame dictionaries for the image space (“framed frames” – wavelets, curvelets, shearlets, etc.). Experiments indicate that data are more compressible in these induced dictionaries when compared to compressibility in terms of principal components.
Nate Strawn, "Framed frames for data frames," Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941A (Presented at SPIE Optical Engineering + Applications: August 08, 2017; Published: 24 August 2017); https://doi.org/10.1117/12.2274778.
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