Approximate kNN Classification for Biomedical Data

Abstract

We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution grows. An indicative example is the single-cell RNA-seq (scRNA-seq), an emerging DNA sequencing technology with promising capabilities but significant computational challenges due to the large-scaled generated data. Regarding the classification process for scRNAseq data, an appropriate method is the k Nearest Neighbor (kNN) classifier since it is usually utilized for large-scale prediction tasks due to its simplicity, minimal parameterization, and model-free nature. However, the ultra-high dimensionality that characterizes scRNA-seq impose a computational bottleneck, while prediction power can be affected by the "Curse of Dimensionality". In this work, we proposed the utilization of approximate nearest neighbor search algorithms for the task of kNN classification in scRNA-seq data focusing on a particular methodology tailored for high dimensional data. We argue that even relaxed approximate solutions will not affect the prediction performance significantly. The experimental results confirm the original assumption by offering the potential for broader applicability.

Motif Cut Sparsifiers

“…Let us discuss this further on a simple example. We may use LSH for an approximate version of a 1-nearest neighbor classifier (see [ABVT20] for a related study), that is, we store the training set in an LSH data structure and on a query we return the label of the (approximate) nearest point in the training set that is returned by LSH. In this setting LSH will satisfy its standard guarantees, when the choice of queries does not depend on previous answers, for example, if one thinks of the queries to originate iid.…”