{"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/sparse-bayesian-approach-for-metric-learning","title":"Sparse Bayesian approach for metric learning in latent space","arxiv_id":null,"date":"2019-08-15","proceeding":"Knowledge-Based Systems 2019 8","authors":["Davood Zabihzadeh","Reza Monsefi","Hadi Sadoghi Yazdi"],"abstract":"This paper presents a new and efficient approach for metric learning in latent space. Our method\r\ndiscovers an optimal mapping from the feature space to a latent space that shrinks the distance\r\nbetween similar data items and also increases the distance between dissimilar ones. The proposed\r\napproach is based on a Bayesian variational framework which iteratively finds the optimal posterior\r\ndistribution of parameters and hyperparameters of the model. Advantages of the proposed method to\r\nsimilar work are 1) Learning the noise of the latent variables on the low-dimensional manifold to find a\r\nmore effective transformation. 2) Automatically finding the dimension of latent space and sparsification\r\nof the solution which prevents the overfitting problem. 3) Unlike Mahalanobis metric learning, the\r\nproposed algorithm roughly scales linearly to the dimension of data. Also, the present work is extended\r\nfor learning in the feature space induced by an RKHS kernel. The proposed method is evaluated on\r\nsmall and large datasets coming from real applications such as network intrusion detection, face\r\nrecognition, handwritten digits, letter recognition, and hyperspectral image classification. The results\r\nshow that our method outperforms related representative and state-of-the-art methods in many small\r\nand large datasets.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S0950705119301741","url_pdf":"https://www.sciencedirect.com/science/article/abs/pii/S0950705119301741","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":"sparse-bayesian-approach-for-metric-learning","repo_url":"https://github.com/GT-Davood/SBML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"intrusion-detection","task_name":"Intrusion Detection"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"network-intrusion-detection","task_name":"Network Intrusion Detection"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}