1,324 research outputs found
Class reconstruction driven adversarial domain adaptation for hyperspectral image classification
We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach
Stratified Transfer Learning for Cross-domain Activity Recognition
In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global domain shift while ignoring the intra-affinity between classes, which
will hinder the performance of the algorithms. In this paper, we propose a
novel and general cross-domain learning framework that can exploit the
intra-affinity of classes to perform intra-class knowledge transfer. The
proposed framework, referred to as Stratified Transfer Learning (STL), can
dramatically improve the classification accuracy for cross-domain activity
recognition. Specifically, STL first obtains pseudo labels for the target
domain via majority voting technique. Then, it performs intra-class knowledge
transfer iteratively to transform both domains into the same subspaces.
Finally, the labels of target domain are obtained via the second annotation. To
evaluate the performance of STL, we conduct comprehensive experiments on three
large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI
DSADS), which demonstrates that STL significantly outperforms other
state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%).
Furthermore, we extensively investigate the performance of STL across different
degrees of similarities and activity levels between domains. And we also
discuss the potential of STL in other pervasive computing applications to
provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready
version
Unsupervised Domain Adaptation via Discriminative Manifold Propagation
Unsupervised domain adaptation is effective in leveraging rich information
from a labeled source domain to an unlabeled target domain. Though deep
learning and adversarial strategy made a significant breakthrough in the
adaptability of features, there are two issues to be further studied. First,
hard-assigned pseudo labels on the target domain are arbitrary and error-prone,
and direct application of them may destroy the intrinsic data structure.
Second, batch-wise training of deep learning limits the characterization of the
global structure. In this paper, a Riemannian manifold learning framework is
proposed to achieve transferability and discriminability simultaneously. For
the first issue, this framework establishes a probabilistic discriminant
criterion on the target domain via soft labels. Based on pre-built prototypes,
this criterion is extended to a global approximation scheme for the second
issue. Manifold metric alignment is adopted to be compatible with the embedding
space. The theoretical error bounds of different alignment metrics are derived
for constructive guidance. The proposed method can be used to tackle a series
of variants of domain adaptation problems, including both vanilla and partial
settings. Extensive experiments have been conducted to investigate the method
and a comparative study shows the superiority of the discriminative manifold
learning framework.Comment: To be published in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces
Identifying meaningful concepts in large data sets can provide valuable
insights into engineering design problems. Concept identification aims at
identifying non-overlapping groups of design instances that are similar in a
joint space of all features, but which are also similar when considering only
subsets of features. These subsets usually comprise features that characterize
a design with respect to one specific context, for example, constructive design
parameters, performance values, or operation modes. It is desirable to evaluate
the quality of design concepts by considering several of these feature subsets
in isolation. In particular, meaningful concepts should not only identify
dense, well separated groups of data instances, but also provide
non-overlapping groups of data that persist when considering pre-defined
feature subsets separately. In this work, we propose to view concept
identification as a special form of clustering algorithm with a broad range of
potential applications beyond engineering design. To illustrate the differences
between concept identification and classical clustering algorithms, we apply a
recently proposed concept identification algorithm to two synthetic data sets
and show the differences in identified solutions. In addition, we introduce the
mutual information measure as a metric to evaluate whether solutions return
consistent clusters across relevant subsets. To support the novel understanding
of concept identification, we consider a simulated data set from a
decision-making problem in the energy management domain and show that the
identified clusters are more interpretable with respect to relevant feature
subsets than clusters found by common clustering algorithms and are thus more
suitable to support a decision maker.Comment: 10 pages, 6 figures, to be published in proceedings of 2022 IEEE
International Conference on Data Mining Workshops (ICDMW
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