1,242 research outputs found
Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence
In this paper we introduce evidence transfer for clustering, a deep learning
method that can incrementally manipulate the latent representations of an
autoencoder, according to external categorical evidence, in order to improve a
clustering outcome. By evidence transfer we define the process by which the
categorical outcome of an external, auxiliary task is exploited to improve a
primary task, in this case representation learning for clustering. Our proposed
method makes no assumptions regarding the categorical evidence presented, nor
the structure of the latent space. We compare our method, against the baseline
solution by performing k-means clustering before and after its deployment.
Experiments with three different kinds of evidence show that our method
effectively manipulates the latent representations when introduced with real
corresponding evidence, while remaining robust when presented with low quality
evidence
VIGAN: Missing View Imputation with Generative Adversarial Networks
In an era when big data are becoming the norm, there is less concern with the
quantity but more with the quality and completeness of the data. In many
disciplines, data are collected from heterogeneous sources, resulting in
multi-view or multi-modal datasets. The missing data problem has been
challenging to address in multi-view data analysis. Especially, when certain
samples miss an entire view of data, it creates the missing view problem.
Classic multiple imputations or matrix completion methods are hardly effective
here when no information can be based on in the specific view to impute data
for such samples. The commonly-used simple method of removing samples with a
missing view can dramatically reduce sample size, thus diminishing the
statistical power of a subsequent analysis. In this paper, we propose a novel
approach for view imputation via generative adversarial networks (GANs), which
we name by VIGAN. This approach first treats each view as a separate domain and
identifies domain-to-domain mappings via a GAN using randomly-sampled data from
each view, and then employs a multi-modal denoising autoencoder (DAE) to
reconstruct the missing view from the GAN outputs based on paired data across
the views. Then, by optimizing the GAN and DAE jointly, our model enables the
knowledge integration for domain mappings and view correspondences to
effectively recover the missing view. Empirical results on benchmark datasets
validate the VIGAN approach by comparing against the state of the art. The
evaluation of VIGAN in a genetic study of substance use disorders further
proves the effectiveness and usability of this approach in life science.Comment: 10 pages, 8 figures, conferenc
Cross-Domain Labeled LDA for Cross-Domain Text Classification
Cross-domain text classification aims at building a classifier for a target
domain which leverages data from both source and target domain. One promising
idea is to minimize the feature distribution differences of the two domains.
Most existing studies explicitly minimize such differences by an exact
alignment mechanism (aligning features by one-to-one feature alignment,
projection matrix etc.). Such exact alignment, however, will restrict models'
learning ability and will further impair models' performance on classification
tasks when the semantic distributions of different domains are very different.
To address this problem, we propose a novel group alignment which aligns the
semantics at group level. In addition, to help the model learn better semantic
groups and semantics within these groups, we also propose a partial supervision
for model's learning in source domain. To this end, we embed the group
alignment and a partial supervision into a cross-domain topic model, and
propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and
Reuters dataset, extensive quantitative (classification, perplexity etc.) and
qualitative (topic detection) experiments are conducted to show the
effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201
Learning to Embed Words in Context for Syntactic Tasks
We present models for embedding words in the context of surrounding words.
Such models, which we refer to as token embeddings, represent the
characteristics of a word that are specific to a given context, such as word
sense, syntactic category, and semantic role. We explore simple, efficient
token embedding models based on standard neural network architectures. We learn
token embeddings on a large amount of unannotated text and evaluate them as
features for part-of-speech taggers and dependency parsers trained on much
smaller amounts of annotated data. We find that predictors endowed with token
embeddings consistently outperform baseline predictors across a range of
context window and training set sizes.Comment: Accepted by ACL 2017 Repl4NLP worksho
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