论文标题
通过无监督的特征提取来改善自组织图
Improving Self-Organizing Maps with Unsupervised Feature Extraction
论文作者
论文摘要
自组织图（SOM）是一种受脑启发的神经模型，对于无监督学习非常有前途，尤其是在嵌入式应用中。但是，在处理复杂数据集时，它无法学习有效的原型。我们建议通过使用提取的功能而不是原始数据来改善SOM性能。我们使用两种不同的方法进行了对SOM分类精度的比较研究：使用基于梯度的学习稀疏的卷积自动编码器的机器学习方法，以及使用峰值定时的神经网络的神经科学方法，使用峰值定时依赖于可变性学习。对SOM进行了训练，以提取的特征进行训练，然后很少使用标记的样品将神经元标记为其相应的类。我们使用不同的特征提取方法研究了特征图，SOM大小和标记子集大小对分类精度的影响。我们将SOM分类提高+6.09 \％，并在无监督的图像分类方面达到最新性能。
The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn efficient prototypes when dealing with complex datasets. We propose in this work to improve the SOM performance by using extracted features instead of raw data. We conduct a comparative study on the SOM classification accuracy with unsupervised feature extraction using two different approaches: a machine learning approach with Sparse Convolutional Auto-Encoders using gradient-based learning, and a neuroscience approach with Spiking Neural Networks using Spike Timing Dependant Plasticity learning. The SOM is trained on the extracted features, then very few labeled samples are used to label the neurons with their corresponding class. We investigate the impact of the feature maps, the SOM size and the labeled subset size on the classification accuracy using the different feature extraction methods. We improve the SOM classification by +6.09\% and reach state-of-the-art performance on unsupervised image classification.
