16 research outputs found
Common Information Components Analysis
We give an information-theoretic interpretation of Canonical Correlation
Analysis (CCA) via (relaxed) Wyner's common information. CCA permits to extract
from two high-dimensional data sets low-dimensional descriptions (features)
that capture the commonalities between the data sets, using a framework of
correlations and linear transforms. Our interpretation first extracts the
common information up to a pre-selected resolution level, and then projects
this back onto each of the data sets. In the case of Gaussian statistics, this
procedure precisely reduces to CCA, where the resolution level specifies the
number of CCA components that are extracted. This also suggests a novel
algorithm, Common Information Components Analysis (CICA), with several
desirable features, including a natural extension to beyond just two data sets.Comment: 5 pages, 1 figure. Presented at the 2020 Information Theory and
Applications (ITA) Workshop, San Diego, CA, USA, February 2-7, 202
On the Semi-supervised Expectation Maximization
The Expectation Maximization (EM) algorithm is widely used as an iterative
modification to maximum likelihood estimation when the data is incomplete. We
focus on a semi-supervised case to learn the model from labeled and unlabeled
samples. Existing work in the semi-supervised case has focused mainly on
performance rather than convergence guarantee, however we focus on the
contribution of the labeled samples to the convergence rate. The analysis
clearly demonstrates how the labeled samples improve the convergence rate for
the exponential family mixture model. In this case, we assume that the
population EM (EM with unlimited data) is initialized within the neighborhood
of global convergence for the population EM that consists solely of samples
that have not been labeled. The analysis for the labeled samples provides a
comprehensive description of the convergence rate for the Gaussian mixture
model. In addition, we extend the findings for labeled samples and offer an
alternative proof for the population EM's convergence rate with unlabeled
samples for the symmetric mixture of two Gaussians.Comment: 7 pages, 0 figure
Sum-Rate Capacity for Symmetric Gaussian Multiple Access Channels with Feedback
The feedback sum-rate capacity is established for the symmetric -user
Gaussian multiple-access channel (GMAC). The main contribution is a converse
bound that combines the dependence-balance argument of Hekstra and Willems
(1989) with a variant of the factorization of a convex envelope of Geng and
Nair (2014). The converse bound matches the achievable sum-rate of the
Fourier-Modulated Estimate Correction strategy of Kramer (2002).Comment: 16 pages, 2 figures, published in International Symposium on
Information Theory (ISIT) 201
Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks
We experimentally solve the problem of maximizing capacity under a total
supply power constraint in a massively parallel submarine cable context, i.e.,
for a spatially uncoupled system in which fiber Kerr nonlinearity is not a
dominant limitation. By using multi-layer neural networks trained with
extensive measurement data acquired from a 12-span 744-km optical fiber link as
an accurate digital twin of the true optical system, we experimentally maximize
fiber capacity with respect to the transmit signal's spectral power
distribution based on a gradient-descent algorithm. By observing convergence to
approximately the same maximum capacity and power distribution for almost
arbitrary initial conditions, we conjecture that the capacity surface is a
concave function of the transmit signal power distribution. We then demonstrate
that eliminating gain flattening filters (GFFs) from the optical amplifiers
results in substantial capacity gains per Watt of electrical supply power
compared to a conventional system that contains GFFs.Comment: arXiv admin note: text overlap with arXiv:1910.0205
Feedback and Common Information: Bounds and Capacity for Gaussian Networks
Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular networks including channel coding such as Gaussian multiple access channel with feedback and source coding such as lossy Gaussian Gray-Wyner network.
On one part, we establish the sum-Capacity of the Gaussian multiple-access channel with feedback. The converse bounds that are derived from the dependence-balance argument of Hekstra and Willems meet the achievable scheme introduced by Kramer. Even though the problem is not convex, the factorization of lower convex envelope method that is introduced by Geng and Nair, combined with a Gaussian property are invoked to compute the sum-Capacity. Additionally, we characterize the rate region of lossy Gaussian Gray-Wyner network for symmetric distortion. The problem is not convex, thus the method of factorization of lower convex envelope is used to show the Gaussian optimality of the auxiliaries. Both of the networks, are a long-standing open problem.
On the other part, we consider the common information that is introduced by Wyner and the natural relaxation of Wyner's common information. Wyner's common information is a measure that quantifies and assesses the commonality between two random variables. The operational significance of the newly introduced quantity is in Gray-Wyner network. Thus, computing the relaxed Wyner's common information is directly connected with computing the rate region in Gray-Wyner network. We derive a lower bound to Wyner's common information for any given source. The bound meets the exact Wyner's common information for sources that are expressed as sum of a common random variable and Gaussian noises. Moreover, we derive an upper bound on an extended variant of information bottleneck.
Finally, we use Wyner's common information and its relaxation as a tool to extract common information between datasets. Thus, we introduce a novel procedure to construct features from data, referred to as Common Information Components Analysis (CICA). We establish that in the case of Gaussian statistics, CICA precisely reduces to Canonical Correlation Analysis (CCA), where the relaxing parameter determines the number of CCA components that are extracted. In this sense, we establish a novel rigorous connection between information measures and CCA, and CICA is a strict generalization of the latter. Moreover, we show that CICA has several desirable features, including a natural extension to beyond just two data sets
Relaxed Wyner's Common Information
In the problem of coded caching for media delivery, two separate coding opportunities have been identified. The first opportunity is a multi-user advantage and crucially hinges on a public broadcast link in the delivery phase. This has been explored in a plethora of works. The second opportunity has received far less attention and concerns similarities between files in the database. Here, the paradigm is to cache "the similarity" between the files. Upon the request, the encoder refines this by providing the specific details for the requested files. Extending Gray and Wyner's work (1974), it follows that the right measure of file similarity is Wyner's Common Information and its generalizations. The present paper surveys and extends the role of Wyner's Common Information in caching. As a novel result, explicit solutions are found for the Gaussian case under mean-squared error, both for the caching problem as well as for the network considered by Gray and Wyner. Our solution leverages and extends the recent technique of factorization of convex envelopes
The Gaussian lossy Gray-Wyner network
We consider the problem of source coding subject to a fidelity criterion for the Gray-Wyner network that connects a single source with two receivers via a common channel and two private channels. The pareto-optimal trade-offs between the sum-rate of the private channels and the rate of the common channel is completely characterized for jointly Gaussian sources subject to the mean-squared error criterion, leveraging convex duality and an argument involving the factorization of convex envelopes. Specifically, it is attained by selecting the auxiliary random variable to be jointly Gaussian with the sources