论文标题
无困难参数T-SNE
Perplexity-free Parametric t-SNE
论文作者
论文摘要
T-分布的随机邻居嵌入（T-SNE）算法是一种普遍使用的维度降低（DR）方法。它的非参数性质和令人印象深刻的功效促使其参数扩展。但是，它与用户定义的困惑参数有限，与最近开发的多尺度无处不在的方法相比，它的DR质量限制了其DR质量。因此，本文提出了一种多尺度参数T-SNE方案，从而摆脱了困惑性调整，并通过深度神经网络实现了映射。它产生可靠的嵌入方式，具有样本外扩展，在多个数据集上的邻里保存方面具有最佳的困惑调整竞争。
The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a ubiquitously employed dimensionality reduction (DR) method. Its non-parametric nature and impressive efficacy motivated its parametric extension. It is however bounded to a user-defined perplexity parameter, restricting its DR quality compared to recently developed multi-scale perplexity-free approaches. This paper hence proposes a multi-scale parametric t-SNE scheme, relieved from the perplexity tuning and with a deep neural network implementing the mapping. It produces reliable embeddings with out-of-sample extensions, competitive with the best perplexity adjustments in terms of neighborhood preservation on multiple data sets.
