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Surv."],"published-print":{"date-parts":[[2022,5,31]]},"abstract":"<jats:p>\n            The past century has witnessed the grand success of brain imaging technologies, such as electroencephalography and magnetic resonance imaging, in probing cognitive states and pathological brain dynamics for neuroscience research and neurology practices. Human brain is \u201cthe most complex object in the universe,\u201d and brain imaging data\u00a0(\n            <jats:italic>BID<\/jats:italic>\n            ) are routinely of multiple\/many attributes and highly non-stationary. These are determined by the nature of\n            <jats:italic>BID<\/jats:italic>\n            as the recordings of the evolving processes of the brain(s) under examination in various views. Driven by the increasingly high demands for precision, efficiency, and reliability in neuro-science and engineering tasks, dimensionality reduction has become a priority issue in\n            <jats:italic>BID<\/jats:italic>\n            analysis to handle the notoriously high dimensionality and large scale of big\n            <jats:italic>BID<\/jats:italic>\n            sets as well as the enormously complicated interdependencies among data elements. This has become particularly urgent and challenging in this big data era.\n          <\/jats:p>\n          <jats:p>\n            Dimensionality reduction theories and methods manifest unrivaled potential in revealing key insights to\n            <jats:italic>BID<\/jats:italic>\n            via offering the low-dimensional\/tiny representations\/features, which may preserve critical characterizations of massive neuronal activities and brain functional and\/or malfunctional states of interest. This study surveys the most salient work along this direction conforming to a 3-dimensional taxonomy with respect to (1) the\n            <jats:italic>scale<\/jats:italic>\n            of\n            <jats:italic>BID<\/jats:italic>\n            , of which the design with this consideration is important for the potential applications; (2) the\n            <jats:italic>order<\/jats:italic>\n            of\n            <jats:italic>BID<\/jats:italic>\n            , in which a higher order denotes more\n            <jats:italic>BID<\/jats:italic>\n            attributes manipulatable by the method; and (3)\n            <jats:italic>linearity<\/jats:italic>\n            , in which the method\u2019s degree of linearity largely determines the \u201cfidelity\u201d in\n            <jats:italic>BID<\/jats:italic>\n            exploration. This study defines criteria for qualitative evaluations of these works in terms of effectiveness, interpretability, efficiency, and scalability. The classifications and evaluations based on the taxonomy provide comprehensive guides to (1) how existing research and development efforts are distributed and (2) their performance, features, and potential in influential applications especially when involving big data. In the end, this study crystallizes the open technical issues and proposes research challenges that must be solved to enable further researches in this area of great potential.\n          <\/jats:p>","DOI":"10.1145\/3448302","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T03:42:46Z","timestamp":1620099766000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Dimensionality Reduction Methods for Brain Imaging Data Analysis"],"prefix":"10.1145","volume":"54","author":[{"given":"Yunbo","family":"Tang","sequence":"first","affiliation":[{"name":"Wuhan University, Wuhan, China"}]},{"given":"Dan","family":"Chen","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}]},{"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Normal University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.101"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2438719"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2017.8050303"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2013.06.006"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.02.040"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.12.015"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2008.0221"},{"key":"e_1_2_2_8_1","volume-title":"Proceedings of the 2008 IEEE International Joint Conference on Neural Networks. 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