5 research outputs found
An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data
Feature selection has been studied widely in the literature. However, the
efficacy of the selection criteria for low sample size applications is
neglected in most cases. Most of the existing feature selection criteria are
based on the sample similarity. However, the distance measures become
insignificant for high dimensional low sample size (HDLSS) data. Moreover, the
variance of a feature with a few samples is pointless unless it represents the
data distribution efficiently. Instead of looking at the samples in groups, we
evaluate their efficiency based on pairwise fashion. In our investigation, we
noticed that considering a pair of samples at a time and selecting the features
that bring them closer or put them far away is a better choice for feature
selection. Experimental results on benchmark data sets demonstrate the
effectiveness of the proposed method with low sample size, which outperforms
many other state-of-the-art feature selection methods.Comment: European Signal Processing Conference 201
Investigating Social Interactions Using Multi-Modal Nonverbal Features
Every day, humans are involved in social situations and interplays, with the goal of
sharing emotions and thoughts, establishing relationships with or acting on other
human beings. These interactions are possible thanks to what is called social intelligence,
which is the ability to express and recognize social signals produced during
the interactions. These signals aid the information exchange and are expressed
through verbal and non-verbal behavioral cues, such as facial expressions, gestures,
body pose or prosody. Recently, many works have demonstrated that social signals
can be captured and analyzed by automatic systems, giving birth to a relatively
new research area called social signal processing, which aims at replicating human
social intelligence with machines. In this thesis, we explore the use of behavioral
cues and computational methods for modeling and understanding social interactions.
Concretely, we focus on several behavioral cues in three specic contexts:
rst, we analyze the relationship between gaze and leadership in small group interactions.
Second, we expand our analysis to face and head gestures in the context of
deception detection in dyadic interactions. Finally, we analyze the whole body for
group detection in mingling scenarios
State discovery for autonomous learning
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (p. 163-171).This thesis is devoted to the study of algorithms for early perceptual learning for an autonomous agent in the presence of feedback. In the framework of associative perceptual learning with indirect supervision, three learning techniques are examined in detail: * short-term on-line memory-based model learning; * long-term on-line distribution-based statistical estimation; * mixed on- and off-line continuous learning of gesture models. The three methods proceed within essentially the same framework, consisting of a perceptual sub-system and a sub-system that implements the associative mapping from perceptual categories to actions. The thesis contributes in several areas - it formulates the framework for solving incremental associative learning tasks; introduces the idea of incremental classification with utility, margin and boundary compression rules; develops a technique of sequence classification with Support Vector Machines; introduces an idea of weak transduction and offers an EM-based algorithm for solving it; proposes a mixed on- and off-line algorithm for learning continuous gesture with reward-based decomposition of the state space. The proposed framework facilitates the development of agents and human-computer interfaces that can be trained by a naive user. The work presented in this dissertation focuses on making these incremental learning algorithms practical.by Yuri A. Ivanov.Ph.D
Vector-Valued Multi-View Semi-Supervsed Learning for Multi-Label Image Classification
Images are usually associated with multiple labels and comprised of multiple views, due to each image containing several objects (e.g. a pedestrian, bicycle and tree) and multiple visual features (e.g. color, texture and shape). Currently available tools tend to use either labels or features for classification, but both are necessary to describe the image properly. There have been recent successes in using vector-valued functions, which construct matrix-valued kernels, to explore the multi-label structure in the output space. This has motivated us to develop multi-view vector-valued manifold regularization (MVMR) in order to integrate multiple features. MVMR exploits the complementary properties of different features, and discovers the intrinsic local geometry of the compact support shared by different features, under the theme of manifold regularization. We validate the effectiveness of the proposed MVMR methodology for image classification by conducting extensive experiments on two challenge datasets, PASCAL VOC' 07 and MIR Flickr