S
Svetha Venkatesh
Researcher at Deakin University
Publications - 864
Citations - 20118
Svetha Venkatesh is an academic researcher from Deakin University. The author has contributed to research in topics: Bayesian optimization & Computer science. The author has an hindex of 60, co-authored 828 publications receiving 16441 citations. Previous affiliations of Svetha Venkatesh include Australian National University & National University of Singapore.
Papers
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Journal ArticleDOI
The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study.
Bridianne O'Dea,Tjeerd W. Boonstra,Mark E. Larsen,Thin Nguyen,Svetha Venkatesh,Helen Christensen +5 more
TL;DR: The authors investigated the associations between linguistic features in individuals' blog data and their symptoms of depression, generalised anxiety, and suicidal ideation, and found that linguistic features observed at the group level may not generalise to, or be useful for, detecting individual symptom change over time.
Journal ArticleDOI
Unified formulation of linear discriminant analysis methods and optimal parameter selection
TL;DR: This paper proposes to unify these LDA variants in one framework: principal component analysis (PCA) plus constrained ridge regression (CRR), which can be viewed as PCA+CRR with particular regularization numbers and class indicators and proposes to choose the best LDA method as choosing the best member from the CRR family.
Proceedings ArticleDOI
Generating comprehensible summaries of rushes sequences based on robust feature matching
Ba Tu Truong,Svetha Venkatesh +1 more
TL;DR: This paper describes the first attempt at tackling a pilot task in Trecvid: video summarization of rushes data, and finds that the method is based on the tight clustering produced via SIFT matching.
Proceedings ArticleDOI
Neighborhood coherence and edge based approaches to film scene extraction
TL;DR: This paper first proposes guidelines from film production to determine when a scene change occurs in film, and examines different rules and conventions followed as part of film grammar to guide and shape the algorithmic solution for determining a scene boundary.
Proceedings ArticleDOI
Towards a Video Browser for the Digital Native
TL;DR: Temporal Semantic Compression is extended for interactive video browsing, which uses an arbitrary frame-by-frame interest measure to sub-sample video in real time, with user interface elements that visualize these measures and the effect of compressing on them.