A multidimensional principal component analysis via the c-product golub–kahan–svd for classification and face recognition

Face recognition and identification are very important applications in machine learning. Due to the increasing amount of available data, traditional approaches based on matricization and matrix PCA methods can be difficult to implement. Moreover, the tensorial approaches are a natural choice, due to the mere structure of the databases, for example in the case of color images. Nevertheless, even though various authors proposed factorization strategies for tensors, the size of the considered tensors can pose some serious issues. Indeed, the most demanding part of the computational effort in recognition or identification problems resides in the training process. When only a few features are needed to construct the projection space, there is no need to compute a SVD on the whole data. Two versions of the tensor Golub–Kahan algorithm are considered in this manuscript, as an alternative to the classical use of the tensor SVD which is based on truncated strategies. In this paper, we consider the Tensor Tubal Golub–Kahan Principal Component Analysis method which purpose it to extract the main features of images using the tensor singular value decomposition (SVD) based on the tensor cosine product that uses the discrete cosine transform. This approach is applied for classification and face recognition and numerical tests show its effectiveness. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Predicting urinary bladder voiding by means of a linear discriminant analysis: Validation in rats

Aims: The objective of this work is to investigate whether changes in bladder pressure's patterns can be used to forecast voiding events in rats with both normal and overactive detrusor. Methods: A voiding forecasting algorithm based on machine learning was developed. Raw pressure curves as well as their corresponding power bands were used as inputs to a linear discriminant analysis classifier. Performance was evaluated on held-out test data and was statistically validated via comparison to random predictors. Results: Using the band-power feature, 93% and 99% of the alarms were respectively generated within 95 s before voiding for normal and hyperactive bladder conditions respectively. The same algorithm was assessed using the band-power feature. It showed performances achieving respective success rates of 99% and 97% for normal and hyperactive bladder condition respectively with alarms generated within 45 s before voiding. Conclusions: We have demonstrated the feasibility of detecting the pre-voiding periods in rats with normal and overactive bladders with a high success rate. Significance: To our knowledge, this is the first study that demonstrates the possibility of predicting voiding in rats with a machine learning algorithm based on a Linear Discriminant Analysis. Our work was compared to other relevant studies and showed better results. With this study, accurate urinary bladder voiding forecasting could be implemented in closed-loop advisory/intervention devices

Mouse oocytes depend on BubR1 for proper chromosome segregation but not for prophase I arrest

International audienceMammalian female meiosis is error prone, with rates of meiotic chromosome missegregations strongly increasing towards the end of the reproductive lifespan. A strong reduction of BubR1 has been observed in oocytes of women approaching menopause and in ovaries of aged mice, which led to the hypothesis that a gradual decline of BubR1 contributes to age-related aneuploidization. Here we employ a conditional knockout approach in mouse oocytes to dissect the meiotic roles of BubR1. We show that BubR1 is required for diverse meiotic functions, including persistent spindle assembly checkpoint activity, timing of meiosis I and the establishment of robust kinetochore–microtubule attachments in a meiosis-specific manner, but not prophase I arrest. These data reveal that BubR1 plays a multifaceted role in chromosome segregation during the first meiotic division and suggest that age-related decline of BubR1 is a key determinant of the formation of aneuploid oocytes as women approach menopause