Clustering under pairwise constraints is an important knowledge discovery
tool that enables the learning of appropriate kernels or distance metrics to
improve clustering performance. These pairwise constraints, which come in the
form of must-link and cannot-link pairs, arise naturally in many applications
and are intuitive for users to provide. However, the common practice of
relaxing discrete constraints to a continuous domain to ease optimization when
learning kernels or metrics can harm generalization, as information which only
encodes linkage is transformed to informing distances. We introduce a new
constrained clustering algorithm that jointly clusters data and learns a kernel
in accordance with the available pairwise constraints. To generalize well, our
method is designed to maximize constraint satisfaction without relaxing
pairwise constraints to a continuous domain where they inform distances. We
show that the proposed method outperforms existing approaches on a large number
of diverse publicly available datasets, and we discuss how our method can scale
to handling large data.

本文提出了一种新的受限聚类算法，该算法可以在考虑已有对比信息的情况下最大程度地满足约束条件，而不会将已知的链接信息转化为距离信息，以此来高效地学习核与度量，并在各种公共数据集上的表现明显优于现有方法。同时，本文还探讨了该方法如何应用于大数据处理，可以有效提高可扩展性。

无需一对一约束松弛的约束聚类和多核学习

Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation

To capture high-speed videos using a two-dimensional detector, video snapshot
compressive imaging (SCI) is a promising system, where the video frames are
coded by different masks and then compressed to a snapshot measurement.
Following this, efficient algorithms are desired to reconstruct the high-speed
frames, where the state-of-the-art results are achieved by deep learning
networks. However, these networks are usually trained for specific small-scale
masks and often have high demands of training time and GPU memory, which are
hence {\bf \em not flexible} to $i$) a new mask with the same size and $ii$) a
larger-scale mask. We address these challenges by developing a Meta Modulated
Convolutional Network for SCI reconstruction, dubbed MetaSCI. MetaSCI is
composed of a shared backbone for different masks, and light-weight
meta-modulation parameters to evolve to different modulation parameters for
each mask, thus having the properties of {\bf \em fast adaptation} to new masks
(or systems) and ready to {\bf \em scale to large data}. Extensive simulation
and real data results demonstrate the superior performance of our proposed
approach. Our code is available at
{\smallhttps://github.com/xyvirtualgroup/MetaSCI-CVPR2021}.

该研究论文提出了一种名为 MetaSCI 的元调制卷积网络，用于高速视频快照压缩成像（video snapshot compressive imaging，简称 SCI）重建，该方法能够快速适应新的掩码并扩展到大规模数据，并在实验中展示出优异性能。