2,188 research outputs found
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
Nearest Neighbors (NN) is one of the most widely used supervised
learning algorithms to classify Gaussian distributed data, but it does not
achieve good results when it is applied to nonlinear manifold distributed data,
especially when a very limited amount of labeled samples are available. In this
paper, we propose a new graph-based NN algorithm which can effectively
handle both Gaussian distributed data and nonlinear manifold distributed data.
To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by
constructing an -level nearest-neighbor strengthened tree over the graph,
and then compute a TRW matrix for similarity measurement purposes. After this,
the nearest neighbors are identified according to the TRW matrix and the class
label of a query point is determined by the sum of all the TRW weights of its
nearest neighbors. To deal with online situations, we also propose a new
algorithm to handle sequential samples based a local neighborhood
reconstruction. Comparison experiments are conducted on both synthetic data
sets and real-world data sets to demonstrate the validity of the proposed new
NN algorithm and its improvements to other version of NN algorithms.
Given the widespread appearance of manifold structures in real-world problems
and the popularity of the traditional NN algorithm, the proposed manifold
version NN shows promising potential for classifying manifold-distributed
data.Comment: 32 pages, 12 figures, 7 table
Self-labeling techniques for semi-supervised time series classification: an empirical study
An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context
A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data
The automatic classification of hyperspectral data is made complex by several factors, such as the high cost of true sample labeling coupled with the high number of spectral bands, as well as the spatial correlation of the spectral signature. In this paper, a transductive collective classifier is proposed for dealing with all these factors in hyperspectral image classification. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. The collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. In particular, the innovative contribution of this study includes: (1) the design of an application-specific co-training schema to use both spectral information and spatial information, iteratively extracted at the object (set of pixels) level via collective inference; (2) the formulation of a spatial-aware example selection schema that accounts for the spatial correlation of predicted labels to augment training sets during iterative learning and (3) the investigation of a diversity class criterion that allows us to speed-up co-training classification. Experimental results validate the accuracy and efficiency of the proposed spectral-spatial, collective, co-training strategy
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the
unlabeled instances of a dataset from a target domain, using labeled instances
of a dataset from a related source domain. In this paper, we propose to cast
this problem in a game-theoretic setting as a non-cooperative game and
introduce a fully automatized iterative algorithm for UDA based on graph
transduction games (GTG). The main advantages of this approach are its
principled foundation, guaranteed termination of the iterative algorithms to a
Nash equilibrium (which corresponds to a consistent labeling condition) and
soft labels quantifying the uncertainty of the label assignment process. We
also investigate the beneficial effect of using pseudo-labels from linear
classifiers to initialize the iterative process. The performance of the
resulting methods is assessed on publicly available object recognition
benchmark datasets involving both shallow and deep features. Results of
experiments demonstrate the suitability of the proposed game-theoretic approach
for solving UDA tasks.Comment: Oral IJCNN 201
Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction
Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification
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