Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
We present a general framework for Bayesian case-based reasoning and prototype classification and clustering -- Latent Case Model (LCM). LCM learns the most representative prototype observations of a dataset by performing joint inference on cluster prototypes and features. Simultaneously, LCM pursue...
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2014
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| Online Access: | http://hdl.handle.net/1721.1/87548 |
| _version_ | 1826200930588033024 |
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| author | Kim, Been Rudin, Cynthia Shah, Julie |
| author2 | Julie A Shah |
| author_facet | Julie A Shah Kim, Been Rudin, Cynthia Shah, Julie |
| author_sort | Kim, Been |
| collection | MIT |
| description | We present a general framework for Bayesian case-based reasoning and prototype classification and clustering -- Latent Case Model (LCM). LCM learns the most representative prototype observations of a dataset by performing joint inference on cluster prototypes and features. Simultaneously, LCM pursues sparsity by learning subspaces, the sets of few features that play important roles in characterizing the prototypes. The prototype and subspace representation preserves interpretability in high dimensional data. We validate the approach preserves classification accuracy on standard data sets, and verify through human subject experiments that the output of LCM produces statistically significant improvements in participants' performance on a task requiring an understanding of clusters within a dataset. |
| first_indexed | 2024-09-23T11:43:57Z |
| id | mit-1721.1/87548 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:43:57Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | mit-1721.1/875482019-04-11T05:21:36Z Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Kim, Been Rudin, Cynthia Shah, Julie Julie A Shah Interactive Robotics Group We present a general framework for Bayesian case-based reasoning and prototype classification and clustering -- Latent Case Model (LCM). LCM learns the most representative prototype observations of a dataset by performing joint inference on cluster prototypes and features. Simultaneously, LCM pursues sparsity by learning subspaces, the sets of few features that play important roles in characterizing the prototypes. The prototype and subspace representation preserves interpretability in high dimensional data. We validate the approach preserves classification accuracy on standard data sets, and verify through human subject experiments that the output of LCM produces statistically significant improvements in participants' performance on a task requiring an understanding of clusters within a dataset. 2014-05-27T18:15:05Z 2014-05-27T18:15:05Z 2014-05-26 2014-05-27T18:15:05Z http://hdl.handle.net/1721.1/87548 MIT-CSAIL-TR-2014-011 10 p. application/pdf |
| spellingShingle | Kim, Been Rudin, Cynthia Shah, Julie Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title | Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_full | Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_fullStr | Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_full_unstemmed | Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_short | Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
| title_sort | latent case model a generative approach for case based reasoning and prototype classification |
| url | http://hdl.handle.net/1721.1/87548 |
| work_keys_str_mv | AT kimbeen latentcasemodelagenerativeapproachforcasebasedreasoningandprototypeclassification AT rudincynthia latentcasemodelagenerativeapproachforcasebasedreasoningandprototypeclassification AT shahjulie latentcasemodelagenerativeapproachforcasebasedreasoningandprototypeclassification |