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Lightweight Conceptual Dictionary Learning for Text Classification Using Information Compression
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DOI：10.1109/TKDE.2024.3421255.png)
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Abstract
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We propose a novel supervised dictionary learning framework for text classification, integrating the Lempel-Ziv-Welch (LZW) algorithm for data compression and dictionary construction. This two-phase approach refines dictionaries by optimizing dictionary atoms for discriminative power using mutual information and class distribution. Our method facilitates classifier training, such as SVMs and neural networks. We introduce the information plane area rank (IPAR) to evaluate the information-theoretic performance of our algorithm. Tested on six benchmark text datasets, our model performs nearly as well as top models in limited-vocabulary settings, lagging by only about 2% while using just 10% of the parameters. However, its performance drops in diverse-vocabulary contexts due to the LZW algorithm's limitations with low-repetition data. This contrast highlights its efficiency and limitations across different dataset types.
Keywords:
Dictionaries
Atoms
Classification algorithms
Text categorization
Neural networks
Accuracy
Vectors
Dictionary learning
information bottleneck
information theory
supervised learning
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