Restricted Boltzmann Machines for Gender Classification

This paper deals with automatic feature learning using a generative model called Restricted Boltzmann Machine (RBM) for the problem of gender recognition in face images. The RBM is presented together with some practical learning tricks to improve the learning capabilities and speedup the training process. The performance of the features obtained is compared against several linear methods using the same dataset and the same evaluation protocol. The results show a classification accuracy improvement compared with classical linear projection methods. Moreover, in order to increase even more the classification accuracy, we have run some experiments where an SVM is fed with the non-linear mapping obtained by the RBM in a tandem configuration.Mansanet Sandin, J.; Albiol Colomer, A.; Paredes Palacios, R.; Villegas, M.; Albiol Colomer, AJ. (2014). Restricted Boltzmann Machines for Gender Classification. Lecture Notes in Computer Science. 8814:274-281. doi:10.1007/978-3-319-11758-4_30S2742818814Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. on PAMI 35(8), 1798–1828 (2013)Bressan, M., Vitrià, J.: Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recognition Letters 24(15), 2743–2749 (2003)Buchala, S., et al.: Dimensionality reduction of face images for gender classification. In: Proceedings of the Intelligent Systems, vol. 1, pp. 88–93 (2004)Cai, D., He, X., Hu, Y., Han, J., Huang, T.: Learning a spatially smooth subspace for face recognition. In: CVPR, pp. 1–7 (2007)Courville, A., Bergstra, J., Bengio, Y.: Unsupervised models of images by spike-and-slab rbms. In: ICML, pp. 1145–1152 (2011)Huang, G.B., et al.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07–49, Univ. of Massachusetts (October 2007)Schmah, T., et al.: Generative versus discriminative training of rbms for classification of fmri images. In: NIPS, pp. 1409–1416 (2008)Graf, A.B.A., Wichmann, F.A.: Gender classification of human faces. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 491–500. Springer, Heidelberg (2002)He, X., Niyogi, P.: Locality preserving projections. In: NIPS (2004)Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)Hinton, G.E.: A practical guide to training restricted boltzmann machines. Technical report, University of Toronto (2010)Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. on PAMI 24(5), 707–711 (2002)Nair, V., Hinton, G.E.: 3d object recognition with deep belief nets. In: NIPS, pp. 1339–1347 (2009)Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: ICML, pp. 791–798 (2007)Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Letters 33(4), 431–437 (2012)Shobeirinejad, A., Gao, Y.: Gender classification using interlaced derivative patterns. In: ICPR, pp. 1509–1512 (2010)Villegas, M., Paredes, R.: Dimensionality reduction by minimizing nearest-neighbor classification error. Pattern Recognition Letters 32(4), 633–639 (2011

Speaker recognition by means of restricted Boltzmann machine adaptation

Restricted Boltzmann Machines (RBMs) have shown success in speaker recognition. In this paper, RBMs are investigated in a framework comprising a universal model training and model adaptation. Taking advantage of RBM unsupervised learning algorithm, a global model is trained based on all available background data. This general speaker-independent model, referred to as URBM, is further adapted to the data of a specific speaker to build speaker-dependent model. In order to show its effectiveness, we have applied this framework to two different tasks. It has been used to discriminatively model target and impostor spectral features for classification. It has been also utilized to produce a vector-based representation for speakers. This vector-based representation, similar to i-vector, can be further used for speaker recognition using either cosine scoring or Probabilistic Linear Discriminant Analysis (PLDA). The evaluation is performed on the core test condition of the NIST SRE 2006 database.Peer ReviewedPostprint (author's final draft

PATTERN: Pain Assessment for paTients who can't TEll using Restricted Boltzmann machiNe.

BackgroundAccurately assessing pain for those who cannot make self-report of pain, such as minimally responsive or severely brain-injured patients, is challenging. In this paper, we attempted to address this challenge by answering the following questions: (1) if the pain has dependency structures in electronic signals and if so, (2) how to apply this pattern in predicting the state of pain. To this end, we have been investigating and comparing the performance of several machine learning techniques.MethodsWe first adopted different strategies, in which the collected original n-dimensional numerical data were converted into binary data. Pain states are represented in binary format and bound with above binary features to construct (n + 1) -dimensional data. We then modeled the joint distribution over all variables in this data using the Restricted Boltzmann Machine (RBM).ResultsSeventy-eight pain data items were collected. Four individuals with the number of recorded labels larger than 1000 were used in the experiment. Number of avaliable data items for the four patients varied from 22 to 28. Discriminant RBM achieved better accuracy in all four experiments.ConclusionThe experimental results show that RBM models the distribution of our binary pain data well. We showed that discriminant RBM can be used in a classification task, and the initial result is advantageous over other classifiers such as support vector machine (SVM) using PCA representation and the LDA discriminant method

From features to speaker vectors by means of restricted Boltzmann machine adaptation

Restricted Boltzmann Machines (RBMs) have shown success in different stages of speaker recognition systems. In this paper, we propose a novel framework to produce a vector-based representation for each speaker, which will be referred to as RBM-vector. This new approach maps the speaker spectral features to a single fixed-dimensional vector carrying speaker-specific information. In this work, a global model, referred to as Universal RBM (URBM), is trained taking advantage of RBM unsupervised learning capabilities. Then, this URBM is adapted to the data of each speaker in the development, enrolment and evaluation datasets. The network connection weights of the adapted RBMs are further concatenated and subject to a whitening with dimension reduction stage to build the speaker vectors. The evaluation is performed on the core test condition of the NIST SRE 2006 database, and it is shown that RBM-vectors achieve 15% relative improvement in terms of EER compared to i-vectors using cosine scoring. The score fusion with i-vector attains more than 24% relative improvement. The interest of this result for score fusion yields on the fact that both vectors are produced in an unsupervised fashion and can be used instead of i-vector/PLDA approach, when no data label is available. Results obtained for RBM-vector/PLDA framework is comparable with the ones from i-vector/PLDA. Their score fusion achieves 14% relative improvement compared to i-vector/PLDA.Peer ReviewedPostprint (published version

A Multiplicative Model for Learning Distributed Text-Based Attribute Representations

In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings. Attributes can correspond to document indicators (to learn sentence vectors), language indicators (to learn distributed language representations), meta-data and side information (such as the age, gender and industry of a blogger) or representations of authors. We describe a third-order model where word context and attribute vectors interact multiplicatively to predict the next word in a sequence. This leads to the notion of conditional word similarity: how meanings of words change when conditioned on different attributes. We perform several experimental tasks including sentiment classification, cross-lingual document classification, and blog authorship attribution. We also qualitatively evaluate conditional word neighbours and attribute-conditioned text generation.Comment: 11 pages. An earlier version was accepted to the ICML-2014 Workshop on Knowledge-Powered Deep Learning for Text Minin