{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dimensionality-reduction-using-similarity","title":"Dimensionality Reduction using Similarity-induced Embeddings","arxiv_id":"1706.05692","date":"2017-06-18","proceeding":null,"authors":["Nikolaos Passalis","Anastasios Tefas"],"abstract":"The vast majority of Dimensionality Reduction (DR) techniques rely on\nsecond-order statistics to define their optimization objective. Even though\nthis provides adequate results in most cases, it comes with several\nshortcomings. The methods require carefully designed regularizers and they are\nusually prone to outliers. In this work, a new DR framework, that can directly\nmodel the target distribution using the notion of similarity instead of\ndistance, is introduced. The proposed framework, called Similarity Embedding\nFramework, can overcome the aforementioned limitations and provides a\nconceptually simpler way to express optimization targets similar to existing DR\ntechniques. Deriving a new DR technique using the Similarity Embedding\nFramework becomes simply a matter of choosing an appropriate target similarity\nmatrix. A variety of classical tasks, such as performing supervised\ndimensionality reduction and providing out-of-of-sample extensions, as well as,\nnew novel techniques, such as providing fast linear embeddings for complex\ntechniques, are demonstrated in this paper using the proposed framework. Six\ndatasets from a diverse range of domains are used to evaluate the proposed\nmethod and it is demonstrated that it can outperform many existing DR\ntechniques.","url_abs":"http://arxiv.org/abs/1706.05692v3","url_pdf":"http://arxiv.org/pdf/1706.05692v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dimensionality-reduction-using-similarity","repo_url":"https://github.com/passalis/sef","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"supervised-dimensionality-reduction","task_name":"Supervised dimensionality reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}