Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation

Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio

Arithmetic coding revisited

Over the last decade, arithmetic coding has emerged as an important compression tool. It is now the method of choice for adaptive coding on multisymbol alphabets because of its speed, low storage requirements, and effectiveness of compression. This article describes a new implementation of arithmetic coding that incorporates several improvements over a widely used earlier version by Witten, Neal, and Cleary, which has become a de facto standard. These improvements include fewer multiplicative operations, greatly extended range of alphabet sizes and symbol probabilities, and the use of low-precision arithmetic, permitting implementation by fast shift/add operations. We also describe a modular structure that separates the coding, modeling, and probability estimation components of a compression system. To motivate the improved coder, we consider the needs of a word-based text compression program. We report a range of experimental results using this and other models. Complete source code is available

Fronthaul data compression for Uplink CoMP in cloud radio access network (C-RAN)

The design of efficient wireless fronthaul connections for future heterogeneous networks incorporating emerging paradigms such as cloud radio access network has become a challenging task that requires the most effective utilisation of fronthaul network resources. In this paper, we propose to use distributed compression to reduce the fronthaul traffic in uplink Coordinated Multi-Point for cloud radio access network. Unlike the conventional approach where each coordinating point quantises and forwards its own observation to the processing centre, these observations are compressed before forwarding. At the processing centre, the decompression of the observations and the decoding of the user message are conducted in a successive manner. The essence of this approach is the optimisation of the distributed compression using an iterative algorithm to achieve maximal user rate with a given fronthaul rate. In other words, for a target user rate the generated fronthaul traffic is minimised. Moreover, joint decompression and decoding is studied and an iterative optimisation algorithm is devised accordingly. Finally, the analysis is extended to multi-user case and our results reveal that, in both dense and ultra-dense urban deployment scenarios, the usage of distributed compression can efficiently reduce the required fronthaul rate and a further reduction is obtained with joint operation