Abstract
Partial Gated Feedback Recurrent Neural Network for Data Compression Type Classification
Highlights
A S the use of digital devices has increased, data exchange has become more convenient, and various types of information can be efficiently stored and transmitted by data compression [1]–[3]
We propose a Partial Gated Feedback Recurrent Neural Network (PGF-recurrent neural network (RNN)) that reflects the characteristics of lossless compression algorithms, such as structural features, indexing methods, and the organization of bitstreams
The main contributions of this paper are summarized as follows: 1) An RNN-based architecture, which extracts the temporal features of compressed text data in consideration with the characteristics of lossless compression algorithms, enables the improved performance of compression type classification; 2) The architecture of PGF-RNN, which groups the hidden states of each timestep and fully-connects them to the group of timestep, helps to generate temporal and spatial features of the compressed data to more effectively determine the characteristics of the compression algorithms; 3) To demonstrate the validity and performance of PGFRNN, we present a comparison of compression type classification accuracy between the gated recurrent unit (GRU)-based method and PGF-RNN and an analysis of the feature vectors
Summary
A S the use of digital devices has increased, data exchange has become more convenient, and various types of information can be efficiently stored and transmitted by data compression [1]–[3]. The main contributions of this paper are summarized as follows: 1) An RNN-based architecture, which extracts the temporal features of compressed text data in consideration with the characteristics of lossless compression algorithms, enables the improved performance of compression type classification; 2) The architecture of PGF-RNN, which groups the hidden states of each timestep and fully-connects them to the group of timestep, helps to generate temporal and spatial features of the compressed data to more effectively determine the characteristics of the compression algorithms; 3) To demonstrate the validity and performance of PGFRNN, we present a comparison of compression type classification accuracy between the gated recurrent unit (GRU)-based method and PGF-RNN and an analysis of the feature vectors.
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