Globally maximizing, locally minimizing : unsupervised discriminant projection with applications to face and palm biometrics

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Classification task-driven efficient feature extraction from tensor data

Automatic classification of complex data is an area of great interest as it allows to make efficient use of the increasingly data intensive environment that characterizes our modern world. This thesis presents to two contributions to this research area. Firstly, the problem of discriminative feature extraction for data organized in multidimensional arrays. In machine learning, Linear Discriminant Analysis (LDA) is a popular discriminative feature extraction method based on optimizing a Fisher type criterion to find the most discriminative data projection. Various extension of LDA to high-order tensor data have been developed. The method proposed is called the Efficient Greedy Feature Extraction method (EGFE). This method avoids solving optimization problems of very high dimension. Also, it can be stopped when the extracted features are deemed to be sufficient for a proper discrimination of the classes. Secondly, an application of EGFE methods to early detection of dementia disease. For the early detection task, four cognitive scores are used as the original data while we employ our greedy feature extraction method to derive discriminative privileged information feature from fMRI data. The results from the experiments presented in this thesis demonstrate the advantage of using privileged information for the early detection task

Representation Learning with Adversarial Latent Autoencoders

A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wisesimilarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon the explicit maximum likelihood training paradigm, as opposed to an implicit one. Likelihood maximization, coupled with poor decoder distribution leads to poor or blurry reconstructions at best. Generative Adversarial Networks (GANs) on the other hand, perform an implicit maximization of the likelihood by solving a minimax game, thus bypassing the issues derived from the explicit maximization. This provides GAN architectures with remarkable generative power, enabling the generation of high-resolution images of humans, which are indistinguishable from real photos to the naked eye. However, GAN architectures lack inference capabilities, which makes them unsuitable for training encoder-decoder maps, effectively limiting their application space.We introduce an autoencoder architecture that (a) is free from the consequences ofmaximizing the likelihood directly, (b) produces reconstructions competitive in quality with state-of-the-art GAN architectures, and (c) allows learning disentangled representations, which makes it useful in a variety of problems. We show that the proposed architecture and training paradigm significantly improves the state-of-the-art in novelty and anomaly detection methods, it enables novel kinds of image manipulations, and has significant potential for other applications

Three Risky Decades: A Time for Econophysics?

Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

The frequency of falls in children judo training

Purpose: Falling techniques are inseparable part of youth judo training. Falling techniques are related to avoiding injuries exercises (Nauta et al., 2013). There is not good evidence about the ratio of falling during the training in children. Methods: 26 children (age 8.88±1.88) were video recorded on ten training sessions for further indirect observation and performance analysis. Results: Research protocol consisted from recording falls and falling techniques (Reguli et al., 2015) in warming up, combat games, falling techniques, throwing techniques and free fighting (randori) part of the training session. While children were taught almost exclusively forward slapping roll, backward slapping roll and sideward direct slapping fall, in other parts of training also other types of falling, as forward fall on knees, naturally occurred. Conclusions: Judo coaches should stress also on teaching unorthodox falls adding to standard judo curriculum (Koshida et al., 2014). Various falling games to teach children safe falling in different conditions should be incorporated into judo training. Further research to gain more data from groups of different age in various combat and non-combat sports is needed

Fear of crime and victimization among the elderly participating in the self-defence course

Purpose. Self-defence training could enhance seniors´ defensive skills and fitness. There is lack of evidence about fear and concerns of seniors participating in the self-defence course. Methods. 18 elderly persons (16 female, 1 male; age 66.2, SD=5.86) participated in the self-defence course lasting 8 training units (each unit 60 minutes). Standardized tool for fear of crime and victimization analysis previously used in Euro-Justis project in the Czech Republic (2011) was used in pretest and posttest. Results. We explored the highest fear of crime by participants in their residence area after dark (mean=2,77; median=3; SD=0,80), lower fear at the night in their homes (mean=2,29; median=2; SD=0,75) and in their residence area at the daytime (mean=2,00; median=2; SD=0,77) at the beginning of the course. We noticed certain decrease of fear of crime after the intervention. Participant were less afraid of crime in their residence area after dark (mean=2,38; median=2; SD=0,77), they felt lower fear of crime at the night in their homes (mean=2,00; median=2; SD=0,48) and in their residence area at the daytime (mean=1,82; median=2; SD=0,63). Conclusions. The approach to self-defence teaching for elderly should be focused not just on the motor development, but also on their emotional state, fear of crime, perception of dangerousness of diverse situations and total wellbeing. Fear of crime analysis can contribute to create tailor made structure of the self-defence course for specific groups of citizens