Neural network-based clustering using pairwise constraints

By Yen-Chang Hsu, Zsolt Kira. 2015

87 citations recorded by the source corpus.

This paper presents a neural network-based end-to-end clustering framework. We design a novel strategy to utilize the contrastive criteria for pushing data-forming clusters directly from raw data, in addition to learning a feature embedding suitable for such clustering. The network is trained with weak labels, specifically partial pairwise relationships between data instances. The cluster assignments and their probabilities are then obtained at the output layer by feed-forwarding the data. The framework has the interesting characteristic that no cluster centers need to be explicitly specified, thus the resulting cluster distribution is purely data-driven and no distance metrics need to be predefined. The experiments show that the proposed approach beats the conventional two-stage method (feature embedding with k-means) by a significant margin. It also compares favorably to the performance of the standard cross entropy loss for classification. Robustness analysis also shows that the method is largely insensitive to the number of clusters. Specifically, we show that the number of dominant clusters is close to the true number of clusters even when a large k is used for clustering.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning to cluster in order to Transfer across domains and tasks (2017, methodology): “Inspired by the work of Hsu & Kira (2016), we construct a contrastive loss for clustering on the probability outputs of a softmax classiﬁer.”
Deep Image Category Discovery using a Transferred Similarity Function (2016, background): “Our work is also relevant to [11], which is a supervised approach for end-to-end clustering.”
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss (2018, methodology): “This includes pairwise-based methods (Hsu and Kira 2016; Fogel et al. 2018; Hsu, Lv, and Kira 2018), and triplet-based methods (Norouzi, Fleet, and Salakhutdinov 2012; Schroff, Kalenichenko, and Philbin 2015; He et al. 2018); all of these require sophisticated methods for sampling training data,…”