63,780 research outputs found
Semi-supervised heterogeneous evolutionary co-clustering
One of the challenges of the machine learning problem is the absence of sufficient number of labeled instances or training instances. At the same time generating labeled data is expensive and time consuming. The semi-supervised approach has shown promising results to solve the problem of insufficient or fewer labeled instance datasets. The key challenge is incorporating the semi-supervised knowledge into the heterogeneous data which is evolving in nature. Most of the prior work that uses semi-supervised knowledge has been performed on heterogeneous static data. The semi-supervised knowledge is incorporated into data which aid the clustering algorithm to obtain better clusters. The semi-supervised knowledge is provided as constrained based or distance based. I am proposing a framework to incorporate prior knowledge to perform co-clustering on the evolving heterogeneous data. This framework can be used to solve a wide range of problems dealing with text analysis, web analysis and image grouping. In the semi-supervised approach we incorporate the domain knowledge by placing the constraints which aid the clustering process in performing effective clustering of the data. In the proposed framework, I am using the constraint based semi-supervised non-negative matrix factorization approach to obtain the co-clustering on the heterogeneous evolving data. The constraint based semi-supervised approach uses the user provided must-link or cannot-link constraints on the central data type before performing co-clustering. To process the original datasets efficiently in terms of time and space I am using the low rank approximation technique to obtain the sparse representation of the input data matrix using the Dynamic Colibri approach
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
- …