Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets
This addresses the issue of data imperfection in graph-based clustering and optimization, offering a more robust method for data analysis tasks, though it appears incremental as it builds directly on network lasso.
The paper tackles the problem of network lasso being sensitive to imperfect data like noise and missing values, which leads to sub-optimal solutions in simultaneous clustering and optimization. The proposed triangle lasso improves robustness by using neighbor-based similarity, and experiments show it yields better performance than state-of-the-art methods in practical scenarios.
Recently, network lasso has drawn many attentions due to its remarkable performance on simultaneous clustering and optimization. However, it usually suffers from the imperfect data (noise, missing values etc), and yields sub-optimal solutions. The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results. In this paper, we propose triangle lasso to avoid its disadvantage. Triangle lasso finds the similar instances according to their neighbours. If two instances have many common neighbours, they tend to become similar. Although some instances are profiled by the imperfect data, it is still able to find the similar counterparts. Furthermore, we develop an efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) to obtain a moderately accurate solution. In addition, we present a dual method to obtain the accurate solution with the low additional time consumption. We demonstrate through extensive numerical experiments that triangle lasso is robust to the imperfect data. It usually yields a better performance than the state-of-the-art method when performing data analysis tasks in practical scenarios.