New subclustering methods to recover data from degraded or corrupted data to improve image and video processing, network analysis, and data privacy applications.
Algorithm to accurately recover the original high-resolution data and cluster data accurately from low quality data.
In this project, we addressed data clustering, one fundamental machine learning problem that groups data points into clusters based on their similarities. We, for the first time, developed a data clustering methods with provable guarantees to recover and cluster data simultaneously from highly quantized measurements. Our research shows that even if the data quality is extremely low, as long as large amounts of data are available, one can still accurately recover the original high-resolution data and cluster the data points accurately. This insight is new and advances state of the art. Our approach applies to image and video processing, network analysis, and data privacy enhancement.