Multimodal semantic analysis with regularized semantic autoencoder

Autor:	Shaily Malik, Poonam Bansal
Rok vydání:	2022
Předmět:	Statistics and Probability Computer science business.industry Semantic analysis (machine learning) General Engineering 020207 software engineering 02 engineering and technology computer.software_genre Autoencoder Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing
Zdroj:	Journal of Intelligent & Fuzzy Systems. 42:909-917
ISSN:	1875-8967 1064-1246
DOI:	10.3233/jifs-189759
Popis:	The real-world data is multimodal and to classify them by machine learning algorithms, features of both modalities must be transformed into common latent space. The high dimensional common space transformation of features lose their locality information and susceptible to noise. This research article has dealt with this issue of a semantic autoencoder and presents a novel algorithm with distinct mapped features with locality preservation into a commonly hidden space. We call it discriminative regularized semantic autoencoder (DRSAE). It maintains the low dimensional features in the manifold to manage the inter and intra-modality of the data. The data has multi labels, and these are transformed into an aware feature space. Conditional Principal label space transformation (CPLST) is used for it. With the two-fold proposed algorithm, we achieve a significant improvement in text retrieval form image query and image retrieval from the text query.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8fa8ea5d6bba1e0ec987079e0c000204 https://doi.org/10.3233/jifs-189759 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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