"Canonical correlation analysis"

SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations

Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we …

Automatic audio tag classification via semi-supervised canonical density estimation

We propose a novel semi-supervised method for building a statistical model that represents the relationship between sounds and text labels (“tags”). The proposed method, named semi-supervised canonical density estimation, makes use of unlabeled sound …

Automatic video annotation via hierarchical topic trajectory model considering cross-modal correlations

We propose a new statistical model, named Hierarchical Topic Trajectory Model (HTTM), for acquiring a dynamically changing topic model that represents the relationship between video frames and associated text labels. Model parameter estimation, …

Semantic indexing and known item search based on a unified model with topic transition representation

We applied a generative approach to the TRECVID 2010 Semantic Indexing (SIN) and Known-Item Search (KIS) tasks, using a probabilistic network called Hierarchical Topic Trajectory Model (HTTM). It is our newly-developed model that can integrate …