Refining Inductive Bias in Unsupervised Learning via Constraints

Algorithmic bias is necessary for learning because it allows a learner to generalize rationally. A bias is composed of all assumptions the learner makes outside of the given data set. There exist some approaches to automatically selecting the best algorithm (and therefore bias) for a problem or automatically shifting bias as learning proceeds. In general, these methods are concerned with supervised learning tasks. However, reducing reliance on supervisory tags or annotations enables the application of learning techniques to many real-world data sets for which no such information exists. We therefore propose the investigation of methods for refining the bias in unsupervised learning algorithms, with the goal of increasing accuracy and improving efficiency. In particular, we will investigate the incorporation of background knowledge in the form of constraints that allow an unsupervised algorithm to automatically avoid unpromising areas of the hypothesis space. Background Knowledge as Constraints. There is a natural connection between the bias in an algorithm and background knowledge. Often, the bias hardcoded into an algorithm was chosen due to background knowledge about the class of tasks to be targeted. This bias encodes certain assumptions about what sort of hypotheses are valid solutions for any problem it is applied to. However, for a specific task it is often the case that more precise information is available that can be used to augment the bias in useful ways. In such cases, it is desirable to leverage this background knowledge to refine the algorithmic bias in the proper direction. In particular, we are interested in improvements that can be obtained with the addition of problem-specific constraints. Constraints are derived from background knowledge and specify relationships between instances that may not be expressible in the traditional feature-value representation used for machine learning data sets. Current and Proposed Work. To date, we have investigated the incorporation of instance-level hard constraints into one clustering algorithm (a partitioning variation of COBWEB (Fisher 1987)). We found that incorporating constraints results in improved clustering accuracy (Wagstaff & Cardie in press). The types of constraints investigated were specific to algorithms that create flat partitions of the input data. We plan to investigate the relative merits of dif-