  Feature transformation is to derive a new feature set from original features
to augment the AI power of data. In many science domains such as material
performance screening, while feature transformation can model material formula
interactions and compositions and discover performance drivers, supervised
labels are collected from expensive and lengthy experiments. This issue
motivates an Unsupervised Feature Transformation Learning (UFTL) problem. Prior
literature, such as manual transformation, supervised feedback guided search,
and PCA, either relies on domain knowledge or expensive supervised feedback, or
suffers from large search space, or overlooks non-linear feature-feature
interactions. UFTL imposes a major challenge on existing methods: how to design
a new unsupervised paradigm that captures complex feature interactions and
avoids large search space? To fill this gap, we connect graph, contrastive, and
generative learning to develop a measurement-pretrain-finetune paradigm for
UFTL. For unsupervised feature set utility measurement, we propose a feature
value consistency preservation perspective and develop a mean discounted
cumulative gain like unsupervised metric to evaluate feature set utility. For
unsupervised feature set representation pretraining, we regard a feature set as
a feature-feature interaction graph, and develop an unsupervised graph
contrastive learning encoder to embed feature sets into vectors. For generative
transformation finetuning, we regard a feature set as a feature cross sequence
and feature transformation as sequential generation. We develop a deep
generative feature transformation model that coordinates the pretrained feature
set encoder and the gradient information extracted from a feature set utility
evaluator to optimize a transformed feature generator.
