Kernel Adaptive Filtering (KAF) are mathematically principled methods which
search for a function in a Reproducing Kernel Hilbert Space. While they work
well for tasks such as time series prediction and system identification they
are plagued by a linear relationship between number of training samples and
model size, hampering their use on the very large data sets common in today's
data saturated world. Previous methods try to solve this issue by
sparsification. We describe a novel view of optimal filtering which may provide
a route towards solutions in a RKHS which do not necessarily have this linear
growth in model size. We do this by defining a RKHS in which the time structure
of a stochastic process is still present. Using correntropy [11], an extension
of the idea of a covariance function, we create a time based functional which
describes some potentially nonlinear desired mapping function. This form of a
solution may provide a fruitful line of research for creating more efficient
representations of functionals in a RKHS, while theoretically providing
computational complexity in the test set similar to Wiener solution.

利用根据时间结构定义的相干信息、扩展的协方差函数以及时间基函数，为在 Reproducing Kernel Hilbert Space 中创建更高效的函数表示提供了一个有希望的研究方向。

RKHS 中最优滤波的另一种观点

An Alternate View on Optimal Filtering in an RKHS

Predicting the trajectory of pedestrians in crowd scenarios is indispensable
in self-driving or autonomous mobile robot field because estimating the future
locations of pedestrians around is beneficial for policy decision to avoid
collision. It is a challenging issue because humans have different walking
motions and the interactions between humans and objects in the current
environment, especially between human themselves, are complex. Previous
researches have focused on how to model the human-human interactions, however,
neglecting the relative importance of interactions. In order to address this
issue, we introduce a novel mechanism based on the correntropy, which not only
can measure the relative importance of human-human interactions, but also can
build personal space for each pedestrian. We further propose an Interaction
Module including this data-driven mechanism that can effectively extract
feature representations of dynamic human-human interactions in the scene and
calculate corresponding weights to represent the importance of different
interactions. To share such social messages among pedestrians, we design an
interaction-aware architecture based on the Long Short-Term Memory (LSTM)
network for trajectory prediction. We demonstrate the performance of our model
on two public datasets and the experimental results demonstrate that our model
can achieve better performance than several latest methods with good
performance.

自动驾驶或自主移动机器人领域中，预测拥挤场景中行人的轨迹是不可或缺的，因为估计周围行人的未来位置有助于制定政策决策以避免碰撞。本文介绍了一种基于 correntropy 的新机制，不仅可以衡量人与人之间交互的相对重要性，还可以为每个行人建立个人空间。我们进一步提出了一种包含这种数据驱动机制的相互作用模块，可以有效地提取场景中动态人与人之间交互的特征表示，并计算相应的权重来表示不同交互的重要性。为了在行人之间共享这些社交信息，我们设计了一种基于 Long Short-Term Memory（LSTM）网络的相互作用感知架构来进行轨迹预测。我们在两个公共数据集上展示了我们模型的性能，实验结果表明我们的模型可以达到比几种最新方法更好的性能。

IA-LSTM: 交互感知的长短期记忆网络用于行人轨迹预测

IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction

In this paper, we study the robust subspace clustering problem, which aims to
cluster the given possibly noisy data points into their underlying subspaces. A
large pool of previous subspace clustering methods focus on the graph
construction by different regularization of the representation coefficient. We
instead focus on the robustness of the model to non-Gaussian noises. We propose
a new robust clustering method by using the correntropy induced metric, which
is robust for handling the non-Gaussian and impulsive noises. Also we further
extend the method for handling the data with outlier rows/features. The
multiplicative form of half-quadratic optimization is used to optimize the
non-convex correntropy objective function of the proposed models. Extensive
experiments on face datasets well demonstrate that the proposed methods are
more robust to corruptions and occlusions.

研究鲁棒子空间聚类问题，提出使用基于 correntropy 的度量方法解决非高斯和冲击噪声问题，进一步扩展方法处理异常值数据，实验证明该方法更加鲁棒。

基于熵相关的 L2 图的稳健子空间聚类

Correntropy Induced L2 Graph for Robust Subspace Clustering

We propose a new information theoretic metric for finding periodicities in
stellar light curves. Light curves are astronomical time series of brightness
over time, and are characterized as being noisy and unevenly sampled. The
proposed metric combines correntropy (generalized correlation) with a periodic
kernel to measure similarity among samples separated by a given period. The new
metric provides a periodogram, called Correntropy Kernelized Periodogram (CKP),
whose peaks are associated with the fundamental frequencies present in the
data. The CKP does not require any resampling, slotting or folding scheme as it
is computed directly from the available samples. CKP is the main part of a
fully-automated pipeline for periodic light curve discrimination to be used in
astronomical survey databases. We show that the CKP method outperformed the
slotted correntropy, and conventional methods used in astronomy for periodicity
discrimination and period estimation tasks, using a set of light curves drawn
from the MACHO survey. The proposed metric achieved 97.2% of true positives
with 0% of false positives at the confidence level of 99% for the periodicity
discrimination task; and 88% of hits with 11.6% of multiples and 0.4% of misses
in the period estimation task.

提出了一种基于信息理论的新度量学方法，称为 Correntropy Kernelized Periodogram（CKP），通过将交叉熵与周期核相结合，对光度曲线的周期性进行识别，并可以直接从可用样本中计算，用于自动化地鉴别灯变曲线中的周期性，从马洛调查绘制的一组光度曲线中表现出优异的性能。