Channel charting is an unsupervised learning task whose objective is to
encode channels so that the obtained representation reflects the relative
spatial locations of the corresponding users. It has many potential
applications, ranging from user scheduling to proactive handover. In this
paper, a channel charting method is proposed, based on a distance measure
specifically designed to reduce the effect of small scale fading, which is an
irrelevant phenomenon with respect to the channel charting task. A nonlinear
dimensionality reduction technique aimed at preserving local distances (Isomap)
is then applied to actually get the channel representation. The approach is
empirically validated on realistic synthetic \new{multipath} MIMO channels,
achieving better results than previously proposed approaches, at a lower cost.

本文提出了一种基于距离度量的信道编码方法，并使用非线性降维技术在实际综合信道上进行经验验证，证明该方法优于先前提出的方法，成本更低。

通过无相位距离计算高效通道制图

Efficient channel charting via phase-insensitive distance computation

Clustering high-dimensional datasets is hard because interpoint distances
become less informative in high-dimensional spaces. We present a clustering
algorithm that performs nonlinear dimensionality reduction and clustering
jointly. The data is embedded into a lower-dimensional space by a deep
autoencoder. The autoencoder is optimized as part of the clustering process.
The resulting network produces clustered data. The presented approach does not
rely on prior knowledge of the number of ground-truth clusters. Joint nonlinear
dimensionality reduction and clustering are formulated as optimization of a
global continuous objective. We thus avoid discrete reconfigurations of the
objective that characterize prior clustering algorithms. Experiments on
datasets from multiple domains demonstrate that the presented algorithm
outperforms state-of-the-art clustering schemes, including recent methods that
use deep networks.

通过深度自编码器实现非线性降维和聚类，优化作为聚类过程的自编码器，避免之前聚类算法中离散目标的缺陷，无需先验知识即可进行聚类，实验证明该算法优于状态的聚类方案并具有普适性。

深度连续聚类

Deep Continuous Clustering

High dimensional time series are endemic in applications of machine learning
such as robotics (sensor data), computational biology (gene expression data),
vision (video sequences) and graphics (motion capture data). Practical
nonlinear probabilistic approaches to this data are required. In this paper we
introduce the variational Gaussian process dynamical system. Our work builds on
recent variational approximations for Gaussian process latent variable models
to allow for nonlinear dimensionality reduction simultaneously with learning a
dynamical prior in the latent space. The approach also allows for the
appropriate dimensionality of the latent space to be automatically determined.
We demonstrate the model on a human motion capture data set and a series of
high resolution video sequences.

本文介绍了一种非线性概率变分方法 - 变分高斯过程动态系统来处理高维时间序列数据中的非线性降维问题，同时在潜空间中学习动态先验，并允许自动确定潜在空间的维数，该方法在人体运动捕捉数据集和一系列高分辨率视频序列上进行了演示。

变分高斯过程动态系统

Variational Gaussian Process Dynamical Systems

Nonlinear dimensionality reduction (NLDR) algorithms such as Isomap, LLE and
Laplacian Eigenmaps address the problem of representing high-dimensional
nonlinear data in terms of low-dimensional coordinates which represent the
intrinsic structure of the data. This paradigm incorporates the assumption that
real-valued coordinates provide a rich enough class of functions to represent
the data faithfully and efficiently. On the other hand, there are simple
structures which challenge this assumption: the circle, for example, is
one-dimensional but its faithful representation requires two real coordinates.
In this work, we present a strategy for constructing circle-valued functions on
a statistical data set. We develop a machinery of persistent cohomology to
identify candidates for significant circle-structures in the data, and we use
harmonic smoothing and integration to obtain the circle-valued coordinate
functions themselves. We suggest that this enriched class of coordinate
functions permits a precise NLDR analysis of a broader range of realistic data
sets.

本文提出了利用统计数据集构建圆形值函数的方法，并使用持久性上同调的机器来识别数据中显著的圆形结构，通过谐波平滑和积分得到圆值坐标函数，进一步拓宽了坐标函数的类别，以更精确地进行 NLDR 分析。