Big data mining is well known to be an important task for data science,
because it can provide useful observations and new knowledge hidden in given
large datasets. Proximity-based data analysis is particularly utilized in many
real-life applications. In such analysis, the distances to k nearest neighbors
are usually employed, thus its main bottleneck is derived from data retrieval.
Much efforts have been made to improve the efficiency of these analyses.
However, they still incur large costs, because they essentially need many data
accesses. To avoid this issue, we propose a machine-learning technique that
quickly and accurately estimates the k-NN distances (i.e., distances to the k
nearest neighbors) of a given query. We train a fully connected neural network
model and utilize pivots to achieve accurate estimation. Our model is designed
to have useful advantages: it infers distances to the k-NNs at a time, its
inference time is O(1) (no data accesses are incurred), but it keeps high
accuracy. Our experimental results and case studies on real datasets
demonstrate the efficiency and effectiveness of our solution.

提出了一种利用全连接神经网络模型和支点来快速准确地估计查询的 k-NN 距离的机器学习技术。实验结果表明，该方法在大数据挖掘和近距离数据分析方面具有高效和有效的优势。

学习的 k-NN 距离估计

Learned k-NN Distance Estimation

In this paper we propose a novel framework for decentralized, online learning
by many learners. At each moment of time, an instance characterized by a
certain context may arrive to each learner; based on the context, the learner
can select one of its own actions (which gives a reward and provides
information) or request assistance from another learner. In the latter case,
the requester pays a cost and receives the reward but the provider learns the
information. In our framework, learners are modeled as cooperative contextual
bandits. Each learner seeks to maximize the expected reward from its arrivals,
which involves trading off the reward received from its own actions, the
information learned from its own actions, the reward received from the actions
requested of others and the cost paid for these actions - taking into account
what it has learned about the value of assistance from each other learner. We
develop distributed online learning algorithms and provide analytic bounds to
compare the efficiency of these with algorithms with the complete knowledge
(oracle) benchmark (in which the expected reward of every action in every
context is known by every learner). Our estimates show that regret - the loss
incurred by the algorithm - is sublinear in time. Our theoretical framework can
be used in many practical applications including Big Data mining, event
detection in surveillance sensor networks and distributed online recommendation
systems.

本文提出了一种新的分布式在线学习框架，将学习者建模为合作的情境赌博机，分析了分布式在线学习算法和完全知识基准的效率，研究表明后者在时间上失误是亚线性的，该理论框架可用于许多实际应用中，包括大数据挖掘、监视传感器网络事件检测和分布式在线推荐系统。