{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/152853"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/152853","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Permutation-based Significance Tests for Multi-modal Hierarchical Dirichlet Processes with Application to Audio-visual Data","abstract":"Complex underlying distributions in multi-modal data motivate the need for data fusion methods that integrate observations of different modalities in a meaningful way. We explore the multi-modal hierarchical Dirichlet process (mmHDP) mixture model as a Bayesian non-parametric approach to data fusion. In particular, we elaborate on its censored-data perspective, which aligns groups of observations at a group level to accommodate for missing data in any modality. To explore the model behavior, we develop a processing pipeline that applies the mmHDP to audio-visual data, a common and practical multi-modal system. We apply this pipeline to musical data with known audio-visual relationships and provide in-depth qualitative analyses on the learned model parameters. Because of its non-parametric and unsupervised clustering nature, it can be difficult to quantify the significance of the learned mmHDP structure. We propose a novel permutation testing framework that empirically measures the significance of the mmHDP structure and demonstrate its viability using both synthetic and real audio-visual data. The results convey that the mmHDP model captures meaningful structure in the audio-visual data and that the permutation testing framework is a viable method for quantifying model significance.","abstract_html":"Complex underlying distributions in multi-modal data motivate the need for data fusion methods that integrate observations of different modalities in a meaningful way. We explore the multi-modal hierarchical Dirichlet process (mmHDP) mixture model as a Bayesian non-parametric approach to data fusion. In particular, we elaborate on its censored-data perspective, which aligns groups of observations at a group level to accommodate for missing data in any modality. To explore the model behavior, we develop a processing pipeline that applies the mmHDP to audio-visual data, a common and practical multi-modal system. We apply this pipeline to musical data with known audio-visual relationships and provide in-depth qualitative analyses on the learned model parameters. Because of its non-parametric and unsupervised clustering nature, it can be difficult to quantify the significance of the learned mmHDP structure. We propose a novel permutation testing framework that empirically measures the significance of the mmHDP structure and demonstrate its viability using both synthetic and real audio-visual data. The results convey that the mmHDP model captures meaningful structure in the audio-visual data and that the permutation testing framework is a viable method for quantifying model significance.","abstract_has_math":false,"creators":["Anderson, Madeline Loui"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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The results convey that the mmHDP model captures meaningful structure in the audio-visual data and that the permutation testing framework is a viable method for quantifying model significance."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Permutation-based Significance Tests for Multi-modal Hierarchical Dirichlet Processes with Application to Audio-visual Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Fisher III, John W."],"dc:contributor.department":["Massachusetts Institute of Technology. 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We apply this pipeline to musical data with known audio-visual relationships and provide in-depth qualitative analyses on the learned model parameters. Because of its non-parametric and unsupervised clustering nature, it can be difficult to quantify the significance of the learned mmHDP structure. We propose a novel permutation testing framework that empirically measures the significance of the mmHDP structure and demonstrate its viability using both synthetic and real audio-visual data. The results convey that the mmHDP model captures meaningful structure in the audio-visual data and that the permutation testing framework is a viable method for quantifying model significance."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/152853"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Permutation-based Significance Tests for Multi-modal Hierarchical Dirichlet Processes with Application to Audio-visual Data"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:20:49Z"}