Performance improvement of the Compressive Classifier using equi-norm tight frames

Finite tight frames play an important role in miscellaneous areas, including quantum information theory. Here we apply a class of tight frames, equiangular tight frames, to address the problem of detecting the entanglement of bipartite states. Here we derive some entanglement criteria based on positive operator-valued measurements built from equiangular tight frames. We also present a class of entanglement witnesses based on the equiangular tight frames. At last, we generalize the entanglement criterion for bipartite systems to multipartite systems.

An equiangular tight frame (ETF) is an M N matrix which has orthogonal equal norm rows, equal normcolumns, and the inner products of all pairs of columns have the same modulus. In this paper we studyETFs in which all of the entries are unimodular, and in particular pth roots of unity. A new constructionof unimodular ETFs based on resolvable Steiner systems is presented. This construction gives many newexamples of unimodular ETFs. In particular, an new in nite class of ETFs with entries in f1; 1g ispresented.Keywords: tight frames, equiangular, Hadamard matrices, Steiner systems, resolvable 1. INTRODUCTION Let F be an M N matrix with real or complex entries. If there exists A > 0 such that FF = AI , where Iis the identity matrix, then F is called a tight frame . If F F has constant diagonal and all of the o -diagonalentries have the same modulus, then F is called equiangular. If all of the entries of F have modulus 1 thenwe call F unimodular . If all of the entries of F are pth roots of unity, then we call F unimodular of degree p.This article is concerned with equiangular tight frames (ETFs) which are unimodular of degree p. Inparticular, we are concerned with nding unimodular ETFs of degree 2, which we shall also refer to asHadamard ETFs. Note that in the usual de nition of ETFs the columns are taken to have norm 1. However,since we are concerned with unimodular ETFs we will avoid normalizing, thus the columns of an M Nunimodular ETF have norm M .The paper is organized as follows. In Section 2 we present some necessary conditions on the existence ofunimodular ETFs. In Section 3 we review the construction of harmonic ETFs, which is an important classof ETFs that also happen to be unimodular. In Section 4 we review the construction of the Steiner ETFs ofFickus, Mixon, and Tremain,

The relationship between equiangular tight frames and strongly regular graphs has been known for several years. This relationship has been exploited to construct many of the latest examples of new strongly regular graphs. Recently it was shown that there is a similar relationship between two-distance tight frames and strongly regular graphs. In this paper we present a new tensor like construction of two-distance tight frames, and hence a family of strongly regular graphs. While graphs with these parameters were known to exist, this new construction is very simple, requiring only the existence of an affine plane, whereas the original constructions often require more complicated objects such as generalized quadrangles.

We present the theory of equiangular tight frames (ETFs) from association schemes as a generalization of harmonic ETFs. This theory has the pleasing consequence of including not only harmonic ETFs from abelian groups but also real ETFs with centroidal symmetry and the ETFs discovered by Renes and Strohmer coming from skew-symmetric conference matrices. Finally we present a theory of nonabelian harmonic ETFs from the perspective of association schemes. An infinite class of ETFs arising as group frames from nonabelian groups is presented.

Most of the recent compressive sensing (CS) literature has focused on sparse signal recovery based on compressive measurements. However, exact signal recovery may not be required in certain signal processing applications such as in inference problems. In this paper, we provide performance limits of classification of sparse as well as not necessarily sparse signals based on compressive measurements. When signals are not necessarily sparse, we show that Kullback-Leibler and Chernoff distances between two probability density functions under any two hypotheses are preserved up to a factor of M/N with M(<;N)-length compressive measurements compared to that with N-length original measurements when the pdfs of the original-length observation vectors exhibit certain properties. These results are used to quantify the performance limits in terms of upper and lower bounds on the probability of error in signal classification with M-length compressive measurements. When the signals of interest are sparse in the standard canonical basis, performance limits are derived in terms of lower bounds on the probability of error in classifying sparse signals with any classification rule.

We introduce the study of frames and equiangular lines in classical geometries over finite fields. After developing the basic theory, we give several examples and demonstrate finite field analogs of equiangular tight frames (ETFs) produced by modular difference sets, and by translation and modulation operators. Using the latter, we prove that Gerzon's bound is attained in each unitary geometry of dimension d=22l+1 over the field F32. We also investigate interactions between complex ETFs and those in finite unitary geometries, and we show that every complex ETF implies the existence of ETFs with the same size over infinitely many finite fields.

Measurement design for detecting sparse signals

We consider the problem of sparse signal reconstruction from noisy 1-bit compressed measurements using a statistically dependent signal, as an aid. We assume that this signal does not share joint sparse representation with the sparse signal and call it a heterogeneous side-information. We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip errors after quantization. We propose a generalized approximate message passing-based algorithm for signal reconstruction from noisy 1-bit compressed measurements which leverages the dependence between the signal and the heterogeneous side-information. We model the dependence between signal and heterogeneous side-information using copula functions and show, through numerical experiments, that the proposed algorithm yields a better reconstruction performance than 1-bit CS-based recovery algorithms that do not exploit the side-information.

A SIC is a maximal equiangular tight frame in a finite dimensional Hilbert space. Given a SIC in dimension d, there is good evidence that there always exists an aligned SIC in dimension d(d − 2), having predictable symmetries and smaller equiangular tight frames embedded in them. We provide a recipe for how to calculate sets of vectors in dimension d(d − 2) that share these properties. They consist of maximally entangled vectors in certain subspaces defined by the numbers entering the d dimensional SIC. However, the construction contains free parameters and we have not proven that they can always be chosen so that one of these sets of vectors is a SIC. We give some worked examples that, we hope, may suggest to the reader how our construction can be improved. For simplicity we restrict ourselves to the case of odd dimensions.

Review for "Entropic uncertainty relations from equiangular tight frames and their applications"

Decision letter for "Entropic uncertainty relations from equiangular tight frames and their applications"

We investigate the construction of two-direction tight wavelet frames. First, a sufficient condition for a two-direction refinable function generating two-direction tight wavelet frames is derived. Second, a simple constructive method of two-direction tight wavelet frames is given. Third, based on the obtained two-direction tight wavelet frames, one can construct a symmetric multiwavelet frame easily. Finally, some examples are given to illustrate the results.

An efficient algorithm for designing projection matrix in compressive sensing based on alternating optimization

We consider two problems for adaptive design of compressive measurement matrices for estimating time-varying sparse signals. In the first problem, we fix the number of compressive measurements collected at each time step and design the compressive measurement matrices over time. The goal is to maximize the conditional mutual information between the support of the sparse signal and the measurements. In the second problem, we adaptively select the number of compressive measurements to be taken at each time step and not the entries in the measurement matrices. Once the number of measurements to be taken is determined, the entries are selected according to a prespecified scheme. Here, we optimize a measure that is a combination of the number of measurements and the conditional mutual information between the support of the sparse signal and the measurements at each time step. We formulate both problems as Partially Observable Markov Decision Processes (POMDPs) and use an approximation method known as rollout to find solutions for these problems. The POMDP formulation enables the application of Bellman's principle for optimality in multi-step lookahead design of compressive measurements.

Construction of unimodular tight frames for compressed sensing using majorization-minimization