Auto-tuning unit norm frames

Abstract Finite unit norm tight frames provide Parseval-like decompositions of vectors in terms of redundant components of equal weight. They are known to be robust against additive noise and erasures, and as such, have great potential as encoding schemes. Unfortunately, up to this point, these frames have proven notoriously difficult to construct. Indeed, though the set of all unit norm tight frames, modulo rotations, is known to contain manifolds of nontrivial dimension, we have but a small finite number of known constructions of such frames. In this paper, we present a new iterative algorithm—gradient descent of the frame potential—for increasing the degree of tightness of any finite unit norm frame. The algorithm itself is easy to implement, and it preserves certain group structures present in the initial frame. In the special case where the number of frame elements is relatively prime to the dimension of the underlying space, we show that this algorithm converges to a unit norm tight frame at a linear rate, provided the initial unit norm frame is already sufficiently close to being tight. By slightly modifying this approach, we get a similar, but weaker, result in the non-relatively-prime case, providing an explicit answer to the Paulsen problem: “How close is a frame which is almost tight and almost unit norm to some unit norm tight frame?”

[1]  J. Kovacevic,et al.  Life Beyond Bases: The Advent of Frames (Part I) , 2007, IEEE Signal Processing Magazine.

[2]  Chi-Ren Shyu,et al.  Image Analysis for Mapping Immeasurable Phenotypes in Maize [Life Sciences] , 2007, IEEE Signal Processing Magazine.

[3]  Peter G. Casazza,et al.  Gradient descent of the frame potential , 2009 .

[4]  Peter G. Casazza,et al.  A Physical Interpretation of Tight Frames , 2006 .

[5]  Demetrio Stojanoff,et al.  The Structure of Minimizers of the Frame Potential on Fusion Frames , 2008, 0811.3159.

[6]  Vivek K. Goyal,et al.  Quantized Overcomplete Expansions in IRN: Analysis, Synthesis, and Algorithms , 1998, IEEE Trans. Inf. Theory.

[7]  P. Massey Optimal reconstruction systems for erasures and for the q-potential , 2008, 0805.2917.

[8]  Bernhard G. Bodmann,et al.  The road to equal-norm Parseval frames , 2010 .

[9]  J. Kovacevic,et al.  Life Beyond Bases: The Advent of Frames (Part II) , 2007, IEEE Signal Processing Magazine.

[10]  Peter G. Casazza,et al.  Equal-Norm Tight Frames with Erasures , 2003, Adv. Comput. Math..

[11]  Dustin G. Mixon,et al.  Filter Bank Fusion Frames , 2010, IEEE Transactions on Signal Processing.

[12]  Nate Strawn,et al.  Manifold structure of spaces of spherical tight frames , 2003, math/0307367.

[13]  N. Higham MATRIX NEARNESS PROBLEMS AND APPLICATIONS , 1989 .

[14]  Christopher Heil Harmonic analysis and applications : in honor of John J. Benedetto , 2006 .

[15]  Vivek K Goyal,et al.  Quantized Frame Expansions with Erasures , 2001 .

[16]  John J. Benedetto,et al.  Finite Normalized Tight Frames , 2003, Adv. Comput. Math..

[17]  V. Paulsen,et al.  Optimal frames for erasures , 2004 .

[18]  B. D. Johnson,et al.  Frame potential and finite abelian groups , 2008, 0801.3813.

[19]  Keri Kornelson,et al.  Convolutional frames and the frame potential , 2005 .

[20]  Robert W. Heath,et al.  Generalized Finite Algorithms for Constructing Hermitian Matrices with Prescribed Diagonal and Spectrum , 2005, SIAM J. Matrix Anal. Appl..

[21]  Peter G. Casazza,et al.  Minimizing Fusion Frame Potential , 2009 .

[22]  Pedro G. Massey,et al.  Minimization of convex functionals over frame operators , 2007, Adv. Comput. Math..

[23]  Peter G. Casazza,et al.  Constructing tight fusion frames , 2011 .