Weakly supervised nonnegative matrix factorization for user-driven clustering

作者：Choo Jaegul^*; Lee Changhyun; Reddy Chandan K; Park Haesun

来源：Data Mining and Knowledge Discovery, 2015, 29(6): 1598-1621.

DOI：10.1007/s10618-014-0384-8

• Nonnegative matrix factorization
• Semi-supervised clustering
• User-driven clustering
• Regularization

Clustering high-dimensional data and making sense out of its result is a challenging problem. In this paper, we present a weakly supervised nonnegative matrix factorization (NMF) and its symmetric version that take into account various prior information via regularization in clustering applications. Unlike many other existing methods, the proposed weakly supervised NMF methods provide interpretable and flexible outputs by directly incorporating various forms of prior information. Furthermore, the proposed methods maintain a comparable computational complexity to the standard NMF under an alternating nonnegativity-constrained least squares framework. By using real-world data, we conduct quantitative analyses to compare our methods against other semi-supervised clustering methods. We also present the use cases where the proposed methods lead to semantically meaningful and accurate clustering results by properly utilizing user-driven prior information.

• 出版日期2015-11