{"id":8296,"date":"2021-03-26T10:14:27","date_gmt":"2021-03-26T15:14:27","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/?p=8296"},"modified":"2026-04-07T12:59:02","modified_gmt":"2026-04-07T16:59:02","slug":"divergence-regulated-encoder-network-for-joint-dimensionality-reduction-and-classification","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2021\/03\/26\/divergence-regulated-encoder-network-for-joint-dimensionality-reduction-and-classification\/","title":{"rendered":"DIVERGENCE REGULATED ENCODER NETWORK FOR JOINT DIMENSIONALITY REDUCTION AND CLASSIFICATION"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>In this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network. Motivated by a popular dimensionality reduction approach, t-Distributed Stochastic Neighbor Embedding (t-SNE), our proposed method incorporates a classification loss computed on samples in a low-dimensional embedding space. We compare the learned sample embeddings against coordinates found by t-SNE in terms of classification accuracy and qualitative assessment. We also explore use of various divergence measures in the t-SNE objective. The proposed method has several advantages such as readily embedding out-of-sample points and reducing feature dimensionality while retaining class discriminability. Our results show that the proposed approach maintains and\/or improves classification performance and reveals characteristics of features produced by neural networks that may be helpful for other applications.<\/p>\n<h2>Links:<\/h2>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2012.15764\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-470\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/arxiv.png\" alt=\"arxiv link of the paper\" width=\"90\" height=\"90\" \/><\/a><\/p>\n<p><a href=\"https:\/\/ieeexplore.ieee.org\/document\/9726172\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-426\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee-300x242.jpg\" alt=\"i triple e link of the paper\" width=\"111\" height=\"89\" srcset=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee-300x242.jpg 300w, https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee.jpg 769w\" sizes=\"auto, (max-width: 111px) 100vw, 111px\" \/><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>J. Peeples, S. Walker, C. McCurley, A. Zare, J. Keller, and W. Xu, \u201cDivergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification,\u201d in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 3511305, doi: 10.1109\/LGRS.2022.3156532.\n<\/code><\/pre>\n<pre class=\"verbatim select-on-click\" title=\"click to copy to clipboard\"><code>@Article{peeples2021DREN,\nTitle = {Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification}, \nAuthor = {Joshua Peeples and Sarah Walker and Connor McCurley and Alina Zare and James Keller and Weihuang Xu},  \nJournal = {IEEE Geoscience and Remote Sensing Letters (GRSL)}, \nyear={2022},\nvolume={19},\nnumber={},\npages={1-5},\ndoi={10.1109\/LGRS.2022.3156532}}<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: In this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network. Motivated by a popular dimensionality reduction approach, t-Distributed Stochastic Neighbor Embedding (t-SNE), our proposed method incorporates a classification loss computed on samples in a low-dimensional embedding space. We compare the learned sample embeddings against coordinates found by [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"off","footnotes":"","_links_to":"","_links_to_target":""},"categories":[9,13],"tags":[151,221,223,237,367,431,739],"class_list":["post-8296","post","type-post","status-publish","format-standard","hentry","category-news","category-publication","tag-classification","tag-deep-convolutional-neural-networks","tag-deep-learning","tag-dimensionality-reduction","tag-image-processing","tag-machine-learning","tag-texture"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/8296","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=8296"}],"version-history":[{"count":2,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/8296\/revisions"}],"predecessor-version":[{"id":16453,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/8296\/revisions\/16453"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=8296"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=8296"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=8296"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}