1,752 research outputs found
Integrating Document Clustering and Topic Modeling
Document clustering and topic modeling are two closely related tasks which
can mutually benefit each other. Topic modeling can project documents into a
topic space which facilitates effective document clustering. Cluster labels
discovered by document clustering can be incorporated into topic models to
extract local topics specific to each cluster and global topics shared by all
clusters. In this paper, we propose a multi-grain clustering topic model
(MGCTM) which integrates document clustering and topic modeling into a unified
framework and jointly performs the two tasks to achieve the overall best
performance. Our model tightly couples two components: a mixture component used
for discovering latent groups in document collection and a topic model
component used for mining multi-grain topics including local topics specific to
each cluster and global topics shared across clusters.We employ variational
inference to approximate the posterior of hidden variables and learn model
parameters. Experiments on two datasets demonstrate the effectiveness of our
model.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm
In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Most video-and-language representation learning approaches employ contrastive
learning, e.g., CLIP, to project the video and text features into a common
latent space according to the semantic similarities of text-video pairs.
However, such learned shared latent spaces are not often optimal, and the
modality gap between visual and textual representation can not be fully
eliminated. In this paper, we propose Expectation-Maximization Contrastive
Learning (EMCL) to learn compact video-and-language representations.
Specifically, we use the Expectation-Maximization algorithm to find a compact
set of bases for the latent space, where the features could be concisely
represented as the linear combinations of these bases. Such feature
decomposition of video-and-language representations reduces the rank of the
latent space, resulting in increased representing power for the semantics.
Extensive experiments on three benchmark text-video retrieval datasets prove
that our EMCL can learn more discriminative video-and-language representations
than previous methods, and significantly outperform previous state-of-the-art
methods across all metrics. More encouragingly, the proposed method can be
applied to boost the performance of existing approaches either as a jointly
training layer or an out-of-the-box inference module with no extra training,
making it easy to be incorporated into any existing methods.Comment: Accepted to NeurIPS 202
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