51 research outputs found
A New Geometric Approach to Latent Topic Modeling and Discovery
A new geometrically-motivated algorithm for nonnegative matrix factorization
is developed and applied to the discovery of latent "topics" for text and image
"document" corpora. The algorithm is based on robustly finding and clustering
extreme points of empirical cross-document word-frequencies that correspond to
novel "words" unique to each topic. In contrast to related approaches that are
based on solving non-convex optimization problems using suboptimal
approximations, locally-optimal methods, or heuristics, the new algorithm is
convex, has polynomial complexity, and has competitive qualitative and
quantitative performance compared to the current state-of-the-art approaches on
synthetic and real-world datasets.Comment: This paper was submitted to the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP) 2013 on November 30, 201
Learning to Hash-tag Videos with Tag2Vec
User-given tags or labels are valuable resources for semantic understanding
of visual media such as images and videos. Recently, a new type of labeling
mechanism known as hash-tags have become increasingly popular on social media
sites. In this paper, we study the problem of generating relevant and useful
hash-tags for short video clips. Traditional data-driven approaches for tag
enrichment and recommendation use direct visual similarity for label transfer
and propagation. We attempt to learn a direct low-cost mapping from video to
hash-tags using a two step training process. We first employ a natural language
processing (NLP) technique, skip-gram models with neural network training to
learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a
corpus of 10 million hash-tags. We then train an embedding function to map
video features to the low-dimensional Tag2vec space. We learn this embedding
for 29 categories of short video clips with hash-tags. A query video without
any tag-information can then be directly mapped to the vector space of tags
using the learned embedding and relevant tags can be found by performing a
simple nearest-neighbor retrieval in the Tag2Vec space. We validate the
relevance of the tags suggested by our system qualitatively and quantitatively
with a user study
A Deep Embedding Model for Co-occurrence Learning
Co-occurrence Data is a common and important information source in many
areas, such as the word co-occurrence in the sentences, friends co-occurrence
in social networks and products co-occurrence in commercial transaction data,
etc, which contains rich correlation and clustering information about the
items. In this paper, we study co-occurrence data using a general energy-based
probabilistic model, and we analyze three different categories of energy-based
model, namely, the , and models, which are able to capture
different levels of dependency in the co-occurrence data. We also discuss how
several typical existing models are related to these three types of energy
models, including the Fully Visible Boltzmann Machine (FVBM) (), Matrix
Factorization (), Log-BiLinear (LBL) models (), and the Restricted
Boltzmann Machine (RBM) model (). Then, we propose a Deep Embedding Model
(DEM) (an model) from the energy model in a \emph{principled} manner.
Furthermore, motivated by the observation that the partition function in the
energy model is intractable and the fact that the major objective of modeling
the co-occurrence data is to predict using the conditional probability, we
apply the \emph{maximum pseudo-likelihood} method to learn DEM. In consequence,
the developed model and its learning method naturally avoid the above
difficulties and can be easily used to compute the conditional probability in
prediction. Interestingly, our method is equivalent to learning a special
structured deep neural network using back-propagation and a special sampling
strategy, which makes it scalable on large-scale datasets. Finally, in the
experiments, we show that the DEM can achieve comparable or better results than
state-of-the-art methods on datasets across several application domains
SamBaTen: Sampling-based Batch Incremental Tensor Decomposition
Tensor decompositions are invaluable tools in analyzing multimodal datasets.
In many real-world scenarios, such datasets are far from being static, to the
contrary they tend to grow over time. For instance, in an online social network
setting, as we observe new interactions over time, our dataset gets updated in
its "time" mode. How can we maintain a valid and accurate tensor decomposition
of such a dynamically evolving multimodal dataset, without having to re-compute
the entire decomposition after every single update? In this paper we introduce
SaMbaTen, a Sampling-based Batch Incremental Tensor Decomposition algorithm,
which incrementally maintains the decomposition given new updates to the tensor
dataset. SaMbaTen is able to scale to datasets that the state-of-the-art in
incremental tensor decomposition is unable to operate on, due to its ability to
effectively summarize the existing tensor and the incoming updates, and perform
all computations in the reduced summary space. We extensively evaluate SaMbaTen
using synthetic and real datasets. Indicatively, SaMbaTen achieves comparable
accuracy to state-of-the-art incremental and non-incremental techniques, while
being 25-30 times faster. Furthermore, SaMbaTen scales to very large sparse and
dense dynamically evolving tensors of dimensions up to 100K x 100K x 100K where
state-of-the-art incremental approaches were not able to operate
- β¦