Sparse coding allows the representation of complex data as a linear combination of basis sparse vectors (alternatively called atoms or codewords), a collection of which constitutes a dictionary. Dictionary learning is a learning process aimed at finding a small number of optimal basis vectors for a more accurate representation of the original data. The existing dictionary learning methods do not address the inherent uncertainty of the input data in their learning processes. To compensate for the uncertainty, and to obtain a flexible and effective learning system, we introduce a new adaptive fuzzy dictionary learning (AFDL) method for image classification purposes. The new method iteratively alternates between sparse coding based on a given dictionary and an adaptive fuzzy dictionary learning approach to learn (improve) dictionary atoms. The adjustability of the dictionary and coefficients vectors, in this method, provide us a more accurate and straight representation of input data. AFDL was applied on magnetic resonance images from the cancer image archive datasets, for medical image classification of cancer tumors. Finally, the overall experimental results clearly show that our approach outperforms its rival techniques in terms of accuracy, sensitivity, and specificity. Convergence speed in the experimental results shows that AFDL can achieve its acceptable precision in a reasonable time.

稀疏编码允许将复杂数据表示为基本稀疏矢量（或称为原子或码字）的线性组合，基本稀疏矢量的集合构成字典。字典学习是一种学习过程，旨在找到少量的最佳基础向量，以便更准确地表示原始数据。现有的字典学习方法在其学习过程中并未解决输入数据固有的不确定性。为了补偿不确定性，并获得灵活有效的学习系统，我们引入了一种新的自适应模糊字典学习（AFDL）方法进行图像分类。新方法迭代地在基于给定字典的稀疏编码和自适应模糊字典学习方法之间进行交替学习，以学习（改进）字典原子。在这种方法中，字典和系数向量的可调整性为我们提供了更准确，更直接的输入数据表示。AFDL被应用于来自癌症图像档案数据库的磁共振图像，用于癌症肿瘤的医学图像分类。最后，整体实验结果清楚地表明，我们的方法在准确性，灵敏性和特异性方面优于竞争对手的技术。实验结果的收敛速度表明，AFDL可以在合理的时间内达到其可接受的精度。整体实验结果清楚地表明，我们的方法在准确性，敏感性和特异性方面都优于竞争对手。实验结果的收敛速度表明，AFDL可以在合理的时间内达到其可接受的精度。整体实验结果清楚地表明，我们的方法在准确性，灵敏度和特异性方面都优于竞争对手。实验结果的收敛速度表明，AFDL可以在合理的时间内达到其可接受的精度。