Extracting low-dimensional signals from high-dimensional data

我的工作的主旨是发展的理论基础，高维推理与内在的低维信号。 有一些美丽的开放式问题，其答案可能对数据科学社区很有用。理解这些问题的关键是理解高维空间中结构化信号的几何结构，以及这些信号在随机投影下的作用。我的大部分工作都涉及信号估计，尽管数据缺乏确定性。这可以通过高噪声、极端量化或其环境维度超过数据本身大小的信号来表征。特别地，压缩感测和矩阵补全属于后一类别。在压缩感知中，假设信号是随机采样的，问题是需要多少样本来重建。在矩阵补全中，假设给定一个矩阵的条目的子采样，并要求填充缺失的条目。在这两种情况下，重要的是在信号重构中利用底层低维信号结构。 以下具体项目具有特殊意义。 1.理解矩阵补全和压缩感知中的测量模式：矩阵补全理论在很大程度上假设了条目的随机采样，在压缩感知中也做出了类似的假设。虽然简化概率模型以允许强大的理论是至关重要的，但这种方法有两个明显的缺点：1）在真实的数据中，通常很明显，在采样中存在不反映随机模型的模式; 2）采样模式本身提供信息;基于统一随机模型的方法不会利用这些信息。我打算在我未来的研究中解决这两个问题。 2.测量中的离散化：大数据分析中的一个一般思想是，非常粗略的测量可以一起利用来做出强大的推断，前提是有足够数量的测量，并且信号具有足够小的维度。这个想法在一位压缩感知中非常精确，这是一种极端量化的模型，其中只保留每个测量的符号。 采样的平滑性的缺乏导致新的理论和计算挑战。 3.有用的维数表征：被二次采样的信号的维数或复杂度的适当概念是什么？给定一个任意的信号集，这个维度是否可以以计算有效的方式估计？这是统计信号处理中最重要的问题之一，有着悠久的历史。我的目标是通过将Le Cam和Kolmogorov的经典思想与来自压缩传感文献的现代模型相结合，对这一挑战进行现代化的改造。

The main thrust of my work is to develop the theoretical underpinnings of high-dimensional inference with intrinsically low-dimensional signals.  There are beautiful open questions whose answers could be of much use to the data science community. A key to understanding these questions is to understand the geometry of structured signals in high dimensional space, and how these act under random projections. Much of my work concerns signal estimation despite a lack of certainty in the data. This can be characterized by high-noise, extreme quantization, or a signal whose ambient dimension exceeds the size of the data itself. In particular, compressive sensing and matrix completion fall into this latter category. In compressive sensing, one assumes that the signal is randomly sampled and the question is how few samples are necessary to reconstruct. In matrix completion one assumes that one is given a subsampling of entries of a matrix and asked to fill in the missing entries. In both cases, it is important to utilize an underlying low-dimensional signal structure in signal reconstruction.  The following specific projects are of special interest.  1. Understanding the measurement pattern in matrix completion and compressive sensing: The theory of matrix completion by largely assumes random sampling of entries and similar assumptions are made in compressive sensing. While it is vital to make simplifying probabilistic models to allow powerful theory, there are two apparent drawbacks of this approach: 1) In real data, it is often clear that there is a pattern in the sampling which does not reflect the random model and 2) the sampling pattern itself gives information; this information is not taken advantage of by methods based on a uniform random model. I aim to address these two points with my future research. 2. Discretization in the measurements: A general idea in the analysis of Big Data is that very rough measurements can be leveraged together to make powerful inferences, provided there are a sufficient number of them, and the signal has sufficiently small dimension. This idea is made quite precise in one-bit compressive sensing, a model of extreme quantization, in which only the sign of each measurement is retained.  The lack of smoothness of the sampling leads to new theoretical and computational challenges. 3. Useful characterizations of dimension: What is the appropriate notion of the dimension or complexity of a signal which is being subsampled? And given an arbitrary signal set, can this dimension be estimated in a computationally efficient manner? This is one of the most important questions of statistical signal processing with a long history of approaches. I aim to give a modern twist on this challenge by combining classical ideas of Le Cam and Kolmogorov with modern models stemming from the compressed sensing literature.