15,652 research outputs found
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
We propose a weakly-supervised framework for action labeling in video, where
only the order of occurring actions is required during training time. The key
challenge is that the per-frame alignments between the input (video) and label
(action) sequences are unknown during training. We address this by introducing
the Extended Connectionist Temporal Classification (ECTC) framework to
efficiently evaluate all possible alignments via dynamic programming and
explicitly enforce their consistency with frame-to-frame visual similarities.
This protects the model from distractions of visually inconsistent or
degenerated alignments without the need of temporal supervision. We further
extend our framework to the semi-supervised case when a few frames are sparsely
annotated in a video. With less than 1% of labeled frames per video, our method
is able to outperform existing semi-supervised approaches and achieve
comparable performance to that of fully supervised approaches.Comment: To appear in ECCV 201
Learning with Clustering Structure
We study supervised learning problems using clustering constraints to impose
structure on either features or samples, seeking to help both prediction and
interpretation. The problem of clustering features arises naturally in text
classification for instance, to reduce dimensionality by grouping words
together and identify synonyms. The sample clustering problem on the other
hand, applies to multiclass problems where we are allowed to make multiple
predictions and the performance of the best answer is recorded. We derive a
unified optimization formulation highlighting the common structure of these
problems and produce algorithms whose core iteration complexity amounts to a
k-means clustering step, which can be approximated efficiently. We extend these
results to combine sparsity and clustering constraints, and develop a new
projection algorithm on the set of clustered sparse vectors. We prove
convergence of our algorithms on random instances, based on a union of
subspaces interpretation of the clustering structure. Finally, we test the
robustness of our methods on artificial data sets as well as real data
extracted from movie reviews.Comment: Completely rewritten. New convergence proofs in the clustered and
sparse clustered case. New projection algorithm on sparse clustered vector
Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications
Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification
performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins
Partially-supervised context-specific independence mixture modeling.
Partially supervised or semi-supervised learning refers to machine learning methods which fall between clustering and classification. In the context of clustering, labels can specify link and do-not-link constraints between data points in di erent ways and constrain the resulting clustering solutions. This is a very natural framework for many biological applications as some labels are often available and even very few label greatly improve clustering results. Context-specific independence models constitute a framework for simultaneous mixture estimation and model structure determination to obtain meaningful models for high-dimensional data with many, possibly uninformative, variables. Here we present the first approach for partial learning of CSI models and demonstrate the e ectiveness of modest amounts of labels for simulated data and for protein sub-family determination
EC3: Combining Clustering and Classification for Ensemble Learning
Classification and clustering algorithms have been proved to be successful
individually in different contexts. Both of them have their own advantages and
limitations. For instance, although classification algorithms are more powerful
than clustering methods in predicting class labels of objects, they do not
perform well when there is a lack of sufficient manually labeled reliable data.
On the other hand, although clustering algorithms do not produce label
information for objects, they provide supplementary constraints (e.g., if two
objects are clustered together, it is more likely that the same label is
assigned to both of them) that one can leverage for label prediction of a set
of unknown objects. Therefore, systematic utilization of both these types of
algorithms together can lead to better prediction performance. In this paper,
We propose a novel algorithm, called EC3 that merges classification and
clustering together in order to support both binary and multi-class
classification. EC3 is based on a principled combination of multiple
classification and multiple clustering methods using an optimization function.
We theoretically show the convexity and optimality of the problem and solve it
by block coordinate descent method. We additionally propose iEC3, a variant of
EC3 that handles imbalanced training data. We perform an extensive experimental
analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known
standalone classifiers, 5 ensemble classifiers, and 2 existing methods that
merge classification and clustering) on 13 standard benchmark datasets. We show
that our methods outperform other baselines for every single dataset, achieving
at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than
the best baseline), more resilient to noise and class imbalance than the best
baseline method.Comment: 14 pages, 7 figures, 11 table
Data Clustering And Visualization Through Matrix Factorization
Clustering is traditionally an unsupervised task which is to find natural groupings or clusters in multidimensional data based on perceived similarities among the patterns. The purpose of clustering is to extract useful information
from unlabeled data.
In order to present the extracted useful knowledge obtained by clustering in a meaningful way, data visualization becomes a popular and growing area of research field. Visualization can provide a qualitative overview of large and complex data sets, which help us the desired insight in truly understanding the phenomena of interest in data.
The contribution of this dissertation is two-fold: Semi-Supervised Non-negative Matrix Factorization (SS-NMF) for data clustering/co-clustering and Exemplar-based data Visualization (EV) through matrix factorization. Compared to traditional data mining models,
matrix-based methods are fast, easy to understand and implement, especially suitable to solve large-scale challenging problems in text mining, image grouping, medical diagnosis, and bioinformatics.
In this dissertation, we present two effective matrix-based solutions
in the new directions of data clustering and visualization.
First, in many practical learning domains,
there is a large supply of unlabeled data but limited labeled data, and in most cases it might
be expensive to generate large amounts of labeled data. Traditional clustering algorithms completely ignore these valuable labeled data and thus are inapplicable to these problems. Consequently, semi-supervised clustering, which can incorporate the domain knowledge to guide a clustering algorithm, has become a topic of significant recent interest.
Thus, we develop a Non-negative Matrix Factorization
(NMF) based framework to incorporate prior knowledge into data clustering. Moreover, with the fast growth of Internet and computational technologies in the past decade, many data mining applications have advanced swiftly from the simple clustering of one data type to the co-clustering of multiple data types, usually involving high heterogeneity. To this end, we extend SS-NMF to perform heterogeneous data co-clustering. From a theoretical perspective, SS-NMF for data clustering/co-clustering is mathematically rigorous. The convergence and correctness of our algorithms are proved.
In addition, we discuss the relationship between SS-NMF with other well-known clustering and co-clustering models.
Second, most of current clustering models only provide the centroids (e.g., mathematical means of the clusters)
without inferring the representative exemplars from real data, thus they are unable to better summarize or visualize the raw data.
A new method, Exemplar-based Visualization (EV), is proposed to cluster and visualize an extremely large-scale data.
Capitalizing on recent advances in matrix approximation and factorization, EV provides a means
to visualize large scale data with high accuracy (in
retaining neighbor relations), high efficiency (in computation), and
high flexibility (through the use of exemplars).
Empirically, we demonstrate the superior performance of our matrix-based data clustering and visualization models
through extensive experiments performed on the publicly available large scale data sets
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