Revisiting Nearest Neighbors from a Sparse Signal Approximation View

Explore a Google TechTalk presented by Sarath Shekkizhar that delves into revisiting nearest neighbors from a sparse signal approximation perspective. Gain insights into alternative neighborhood definitions, focusing on non-negative kernel regression (NNK) as an improved and efficient approach. Learn about the interpretation of neighborhoods as sparse signal approximation problems and how this view can enhance graph-based signal processing and machine learning. Discover a k-means-like algorithm leveraging NNK for data summarization and outlier detection. Examine a graph framework for empirically understanding deep neural networks, providing insights into model similarities, differences, invariances, and generalization performance. Explore topics such as kernel similarity, local linearity, basis pursuit, and the geometry of kernel ratio intervals. Witness practical applications through experiments in label propagation and classification, and understand the potential of NNK-Means for detecting representational outliers.