关键词: 深度学习, 自动编码器, 特征提取, 聚类

Abstract: According to the characteristics of short texts, the authors propose a feature extraction and clustering algorithm named deep denoise sparse auto-encoder. The algorithm takes the advantage of deep learning, transforming those high-dimensional, sparse vectors into new, low-dimensional, essential ones. Firstly, L1 paradigm is introduced to avoid overfitting, and the noise is added to improve the robustness. Experimental result shows that applying extracted text features can significantly improve the effectiveness of clustering. It is a valid method to solve the high-dimensional, sparse problem in the short text vector.

Key words: deep learning, auto-encoder, feature extraction, clustering