Feature Space Research Articles

Wages: The Worst Transistor Aging Analysis for Large-scale Analog Integrated Circuits via Domain Generalization

Transistor aging leads to the deterioration of analog circuit performance over time. The worst aging degradation is used to evaluate the circuit reliability. It is extremely expensive to obtain it since several circuit stimuli need to be simulated. The worst degradation collection cost reduction brings an inaccurate training dataset when a machine learning (ML) model is used to fast perform the estimation. Motivated by the fact that there are many similar subcircuits in large-scale analog circuits, in this article we propose Wages to train an ML model on an inaccurate dataset for the worst aging degradation estimation via a domain generalization technique. A sampling-based method on the feature space of the transistor and its neighborhood subcircuit is developed to replace inaccurate labels. A consistent estimation for the worst degradation is enforced to update model parameters. Label updating and model updating are performed alternately to train an ML model on the inaccurate dataset. Experimental results on the very advanced 5 nm technology node show that our Wages can significantly reduce the label collection cost with a negligible estimation error for the worst aging degradations compared to the traditional methods.

Bayesian learning of feature spaces for multitask regression

This paper introduces a novel approach to learn multi-task regression models with constrained architecture complexity. The proposed model, named RFF-BLR, consists of a randomised feedforward neural network with two fundamental characteristics: a single hidden layer whose units implement the random Fourier features that approximate an RBF kernel, and a Bayesian formulation that optimises the weights connecting the hidden and output layers. The RFF-based hidden layer inherits the robustness of kernel methods. The Bayesian formulation enables promoting multioutput sparsity: all tasks interplay during the optimisation to select a compact subset of the hidden layer units that serve as common non-linear mapping for every tasks. The experimental results show that the RFF-BLR framework can lead to significant performance improvements compared to the state-of-the-art methods in multitask nonlinear regression, especially in small-sized training dataset scenarios.

A three-dimensional feature-based fusion strategy for infrared and visible image fusion

Due to the lacking of attention to the scene’s essential characteristics, the existing fusion methods suffer from the deficiency of scene distortion. In addition, the lack of groundtruth can cause an inadequate representation of vital information. To this end, we propose a novel infrared and visible image fusion network based on three-dimensional feature fusion strategy (D3Fuse). In our method, we consider the scene semantic information in the source images and extract the commonality contents of the two images as the third-dimensional feature to extend the feature space for fusion tasks. Specifically, a commonality feature extraction module (CFEM) is designed to extract the scene commonality features. Subsequently, the scene commonality features are utilized together with modality features to construct the fusion image. Moreover, to ensure the independence and diversity of distinct features, we employ a contrastive learning strategy with multiscale PCA coding, which stretches the feature distance in an unsupervised manner, prompting the encoder to extract more discriminative information without incurring additional parameters and computational costs. Furthermore, a contrastive enhancement strategy is utilized to ensure adequate representation of modality information. The results of the qualitative and quantitative evaluations on the three datasets show that the proposed method has better visual performance and higher objective metrics with lower computational cost. The object detection experiments show that our results have superior performance on high-level semantic tasks.

Deformation depth decoupling network for point cloud domain adaptation

Recently, point cloud domain adaptation (DA) practices have been implemented to improve the generalization ability of deep learning models on point cloud data. However, variations across domains often result in decreased performance of models trained on different distributed data sources. Previous studies have focused on output-level domain alignment to address this challenge. But this approach may increase the amount of errors experienced when aligning different domains, particularly for targets that would otherwise be predicted incorrectly. Therefore, in this study, we propose an input-level discretization-based matching to enhance the generalization ability of DA. Specifically, an efficient geometric deformation depth decoupling network (3DeNet) is implemented to learn the knowledge from the source domain and embed it into an implicit feature space, which facilitates the effective constraint of unsupervised predictions for downstream tasks. Secondly, we demonstrate that the sparsity within the implicit feature space varies between domains, rendering domain differences difficult to support. Consequently, we match sets of neighboring points with different densities and biases by differentiating the adaptive densities. Finally, inter-domain differences are aligned by constraining the loss originating from and between the target domains. We conduct experiments on point cloud DA datasets PointDA-10 and PointSegDA, achieving advanced results (over 1.2% and 1% on average).

An improved equilibrium optimization algorithm for feature selection problem in network intrusion detection

In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium optimization (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL) approach is applied by this algorithm to improve the diversity of the population. Also, the Levy flight method is utilized to escape local optima. Then, the binary rendition of the algorithm called BLOEO is employed to feature selection in IDSs. One of the main challenges in IDSs is the high-dimensional feature space, with many irrelevant or redundant features. The BLOEO algorithm is designed to intelligently select the most informative subset of features. The empirical findings on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets demonstrate the effectiveness of the BLOEO algorithm. This algorithm has an acceptable ability to effectively reduce the number of data features, maintaining a high intrusion detection accuracy of over 95%. Specifically, on the UNSW-NB15 dataset, BLOEO selected only 10.8 features on average, achieving an accuracy of 97.6% and a precision of 100%.

A dynamics simulation-assisted transfer learning method for track condition diagnosis in urban rail transits

Track defects are gradually emerging with the development of urban rail transits. However, there is rare research implemented to diagnose track conditions in real time. Although some intelligent data-driven methods seem to have the potential to achieve the track condition diagnosis, it’s hard to acquire sufficient labeled data in actual applications. This study proposes a dynamics simulation-assisted transfer learning (TL) method for label-scarce track condition diagnosis. Firstly, a dynamics model of axle-box bearings considering the service environment is established. Based on this model, a large amount of axle-box vibration signals corresponding to healthy/defective track conditions is simulated. Wavelet transform is performed for these signals to characterize their time-frequency energy distribution modes in the format of time-frequency maps, which are considered source-domain data. Similarly, the time-frequency maps of the collected signals during vehicle operation are served as the target-domain data. Subsequently, a sub-domain alignment TL network is constructed to map the data from the source and target domain into a deep feature space. In this network, unlabeled target-domain data are classified to obtain their pseudo labels. Finally, Wasserstein distance measure and multiple domain discriminators are employed to achieve label alignment between two domains for each corresponding category. A feature centroid-driven loss function is applied to further reduce the intra-class variations, ultimately realizing accurate knowledge transfer from simulated signals to collected signals. A two-level sliding window algorithm is designed to detect abnormal axle-box vibration signal parts which are then diagnosed through the well-trained network. The proposed method is validated through a transfer diagnosis experiment using simulated signals and collected signals. This study provides a promising solution to diagnose different track conditions, which is of great significance for ensuring running safety in urban rail transits.

Exploiting Feature Space Manipulation for Image Generation in Deep Convolutional Generative Adversarial Networks

Exploiting Feature Space Manipulation for Image Generation in Deep Convolutional Generative Adversarial Networks

Research on the Implementation of Financial Risk Control Models and Algorithms Based on Machine Learning

Nowadays, machine learning has been rapidly applied to the financial services industry. With the applications of machine learning, it will bring challenges to the stable development of the financial industry. This paper studies the user behavior analysis algorithm based on operator big data, and provides financial risk control mechanism for financial enterprises. Taking finance as the data collection object, in the user behavior prediction algorithm, the experiment uses the spectral clustering algorithm to cluster the non-convex data samples, and converts the samples to the Laplacian feature space. It not only reduces the dimension, but also clusters the data, and obtains the global optimal solution. The proposed algorithm is analyzed experimentally, and the tool prototype is realized. The developed gadgets have also been applied in enterprises. The research provides a method for the construction of a risk control model in the financial industry, thereby helping financial enterprises to better control the risk of lending. This paper also provides reference for the performance improvement of the model.

Industrial process fault detection based on dynamic kernel principal component analysis combined with weighted structural difference

AbstractThe practical application of traditional data‐driven techniques for process monitoring encounters significant challenges due to the inherent nonlinear and dynamic nature of most industrial processes. Aiming at the problem of nonlinear dynamic process monitoring, a novel fault detection method based on dynamic kernel principal component analysis combined with weighted structural difference (DKPCA‐WSD) is proposed in this paper. Initially, the proposed method leverages a sophisticated nonlinear transformation to project the augmented matrix of the original input data into a high‐dimensional feature space, thereby facilitating the establishment of a DKPCA model. Subsequently, the WSD statistic is computed, utilizing a widely known sliding window technique, to quantify the mean and standard deviation differences across data structures. Ultimately, the WSD statistic is utilized for fault detection, completing the process monitoring task. By integrating the capability of DKPCA to capture nonlinear dynamic characteristics with the effectiveness of the WSD statistic in mitigating the impact of non‐Gaussian data distributions, DKPCA‐WSD significantly enhances the monitoring performance of traditional DKPCA in nonlinear dynamic processes. The proposed method is evaluated through a numerical case exhibiting nonlinear dynamic behaviors and a simulation model of a continuous stirred tank reactor. A comparative analysis with conventional methods, including principal component analysis (PCA), dynamic principal component analysis, KPCA, PCA similarity factor (SPCA), DKPCA, and moving window KPCA (MWKPCA), demonstrates that DKPCA‐WSD outperforms traditional fault detection techniques in nonlinear dynamic processes, offering a substantial improvement in monitoring performance.

AN ADVANCED APPROACH TO THE DEFINITION OF THE “MILKY WAY GALAXIES-ANALOGUES”

Our Galaxy — the Milky Way — has certain features of the structure and evolution. The morphological, photometric, kinematic, and chemodynamical properties are usually considered in search for the Milky Way galaxies-analogues (MWAs). The discovery of MWA galaxies with a larger number of simultaneous selection parameters, as well as more stringent constraints on a given parameter, yields a sample of MWA galaxies with properties closer to the true properties of the Milky Way. So, in general, such MW parameters as the morphological type, luminosity, color indices, structural parameters (size, bar, bulge, thin and thick disks, inner ring, halo), bulge- to-total ratio, stellar mass, star formation rate, metallicity, and rotation velocity were used in various combinations for comparison with other galaxies. However, the offset of some MW features in the multi-parameter space of MWAs features should be significant. The paper aims to give a brief overview of the problematics and to present our approach for studying Milky Way and MWAs match- ing characteristics (this project is supported by the National Research Fund of Ukraine). We propose to enlarge as much as possible the number of Milky Way features and compile various samples of MWAs in our co-moving cosmological volume for their further optimization. Such features can include 3D-kinematics of star’s movement in certain regions, low oxygen content on the periphery, low nuclear activity, and the lack of significant merging over the past 10 Gyrs (isolation criterion). This approach will make it possible to widely formulate the necessary and sufficient conditions for the detection of MWA galaxies as well as to reveal other MW multiwave- length features

Euclidean-Distance-Preserved Feature Reduction for efficient person re-identification

Person Re-identification (Re-ID) aims to match person images across non-overlapping cameras. The existing approaches formulate this task as fine-grained representation learning with deep neural networks, which involves extracting image features using a deep convolutional network, followed by mapping the features into a discriminative space through another smaller network, in order to make full use of all possible cues. However, recent Re-ID methods that strive to capture every cue and make the space more discriminative have resulted in longer features, ranging from 1024 to 14336, leading to higher time (distance computation) and space (feature storage) complexities. There are two potential solutions: reduction-after-training methods (such as Principal Component Analysis and Linear Discriminant Analysis) and reduction-during-training methods (such as 1 × 1 Convolution). The former utilizes a statistical approach aiming for a global optimum but lacking end-to-end optimization of large data and deep neural networks. The latter lacks theoretical guarantees and may be vulnerable to training noise such as dataset noise or initialization seed.To address these limitations, we propose a method called Euclidean-Distance-Preserving Feature Reduction (EDPFR) that combines the strengths of both reduction-after-training and reduction-during-training methods. EDPFR first formulates the feature reduction process as a matrix decomposition and derives a condition to preserve the Euclidean distance between features, thus ensuring accuracy in theory. Furthermore, the method integrates the matrix decomposition process into a deep neural network to enable end-to-end optimization and batch training, while maintaining the theoretical guarantee. The result of the EDPFR is a reduction of the feature dimensions from fa and fb to fa′ and fb′, while preserving their Euclidean distance, i.e.L2(fa,fb)=L2(fa′,fb′). In addition to its Euclidean-Distance-Preserving capability, EDPFR also features a novel feature-level distillation loss. One of the main challenges in knowledge distillation is dimension mismatch. While previous distillation losses, usually project the mismatched features to matched class-level, spatial-level, or similarity-level spaces, this can result in a loss of information and decrease the flexibility and efficiency of distillation. Our proposed feature-level distillation leverages the benefits of the Euclidean-Distance-Preserving property and performs distillation directly in the feature space, resulting in a more flexible and efficient approach. Extensive on three Re-ID datasets, Market-1501, DukeMTMC-reID and MSMT demonstrate the effectiveness of our proposed Euclidean-Distance-Preserving Feature Reduction.

CF‐Net: Cross fusion network for semantic segmentation

AbstractSemantic segmentation is a fundamental computer vision task, and deep learning methods have been successfully applied to this field. However, target morphology continues to exhibit the incomplete prediction problem, which is attributable to the low feature utilisation and the insufficiency of spatial location information. This paper proposes a novel cross fusion network with unit attention mechanism (CF‐Net) for semantic segmentation. The two hallmarks of the framework are the usage of a multi‐scale fusion module and the unit attention mechanism. Multi‐scale fusion module can integrate multi‐branch outputs with different receptive fields, which obtain fine‐grained target details and visual contextual information. The cross fusion network is optimised with a unit attention mechanism to fuse intermediate features, which enables the acquisition of more accurate and effective spatial location information while maintaining consistency in feature space. The experimental results demonstrate that the proposed CF‐Net outperforms favourably comparable with other existing methods on the CamVid, Cityscapes, and PASCAL VOC 2012 databases, which also verifies the Effectiveness and reliability of our method.

Multiple representation contrastive self-supervised learning for pulmonary nodule detection✰

Self-supervised learning aims to create semantic-enriched representation from unannotated data. A prevalent strategy in this field involves training a unified representation space that is invariant to various transformation combinations. However, creating a single invariant representation to multiple transformations poses several challenges. The efficacy of such a representation space depends on factors such as the intensity, sequence, and various combination scenarios of transformations. As a result, features generated in single representation space may exhibit limited adaptability for subsequent tasks. In contrast to the conventional SSL training approach, we introduce a novel method that involves constructing multiple atomic transformation-invariant representation subspaces. Each subspace in the proposed method is invariant to a specific atomic transformation from a predefined reference set. Our method offers increased flexibility by enabling the downstream task to weigh every atomic transformation-invariant subspace based on the desired feature space. A series of experiments were conducted to compare our approach to traditional self-supervised learning methods in order to assess its effectiveness. This evaluation encompassed diverse data regimes, datasets, evaluation protocols, and perspectives on source-destination data distribution. Our results highlight the superiority of our method compared to training strategies based on single transformation-invariant representation spaces. Additionally, our proposed method demonstrated superior performance in reducing false positives in the context of pulmonary nodule detection when compared to several recent supervised and self-supervised approaches.

Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials.

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, we explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases. This study emphasizes the importance of feature analysis and selection in ML modeling and demonstrates the efficacy of our gradient-boosted statistical feature-selection workflow for training predictive models. The models are fine-tuned through Bayesian optimization, using features derived solely from the chemical compositions of the materials data, before the model predictions are evaluated against literature values. We have collated ca. 35,000 Tc values and the performance of our workflow is benchmarked against state-of-the-art algorithms, the results of which demonstrate that our methodology is superior to the majority of alternative methods. In a 10-fold cross-validation, our regression model realized an R2 of (0.92 ± 0.01), an MAE of (40.8 ± 1.9) K, and an RMSE of (80.0 ± 5.0) K. We demonstrate the utility of our ML model through case studies that forecast Tc values for rare-earth intermetallic compounds and generate magnetic phase diagrams for various chemical systems. These case studies highlight the importance of a systematic approach to feature analysis and selection in enhancing both the predictive capability and interpretability of ML models, while being devoid of potential human bias. They demonstrate the advantages of such an approach over a mere reliance on algorithmic complexity and a black-box treatment in ML-based modeling within the domain of computational materials science.

Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation

Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer’s expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder–decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets—including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)—demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics.

Multifractal analysis of heart rate variability in pregnancy during sleep.

Understanding the complex dynamics of heart rate variability (HRV) during pregnancy is crucial for monitoring both maternal well-being and fetal health. In this study, we use the Multifractal Detrended Fluctuations Analysis approach to investigate HRV patterns in pregnant individuals during sleep based on RR interval maxima (MM fluctuations). In addition, we study the type of multifractality within MM fluctuations, that is, if it arises from a broad probability density function or from varying long-range correlations. Furthermore, to provide a comprehensive view of HRV changes during sleep in pregnancy, classical temporal and spectral HRV indices were calculated at quarterly intervals during sleep. Our study population consists of 21 recordings from nonpregnant women, 18 from the first trimester (early-pregnancy) and 18 from the second trimester (middle-pregnancy) of pregnancy. Results. There are statistically significant differences ( -value < 0.05) in mean heart rate, rms heart rate, mean MM fluctuations, and standard deviation of MM fluctuations, particularly in the third and fourth quarter of sleep between pregnant and non-pregnant states. In addition, the early-pregnancy group shows significant differences ( -value < 0.05) in spectral indices during the first and fourth quarter of sleep compared to the non-pregnancy group. Furthermore, the results of our research show striking similarities in the average multifractal structure of MM fluctuations between pregnant and non-pregnant states during normal sleep. These results highlight the influence of different long-range correlations within the MM fluctuations, which could be primarily associated with the emergence of sleep cycles on multifractality during sleep. Finally, we performed a separability analysis between groups using temporal and spectral HRV indices as features per sleep quarter. Employing only three features after Principal Component Analysis (PCA) to the original feature set, achieving complete separability among all groups appears feasible. Using multifractal analysis, our study provides a comprehensive understanding of the complex HRV patterns during pregnancy, which holds promise for maternal and fetal health monitoring. The separability analysis also provides valuable insights into the potential for group differentiation using simple measures such as mean heart rate, rms heart rate, and mean MM fluctuations or in the transformed feature space based on PCA.

Mapping the neurodevelopmental predictors of psychopathology.

Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is anessential steptowards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.

Non‐linear phylogenetic regression using regularised kernels

Abstract Phylogenetic regression is a type of generalised least squares (GLS) method that incorporates a modelled covariance matrix based on the evolutionary relationships between species (i.e. phylogenetic relationships). While this method has found widespread use in hypothesis testing via phylogenetic comparative methods, such as phylogenetic ANOVA, its ability to account for non‐linear relationships has received little attention. To address this, here we implement a phylogenetic Kernel Ridge Regression (phyloKRR) method that utilises GLS in a high‐dimensional feature space, employing linear combinations of phylogenetically weighted data to account for non‐linearity. We analysed two biological datasets using the Radial Basis Function and linear kernel function. The first dataset contained morphometric data, while the second dataset comprised discrete trait data and diversification rates as response variable. Hyperparameter tuning of the model was achieved through cross‐validation rounds in the training set. In the tested biological datasets, phyloKRR reduced the error rate (as measured by RMSE) by around 20% compared to linear‐based regression when data did not exhibit linear relationships. In simulated datasets, the error rate decreased almost exponentially with the level of non‐linearity. These results show that introducing kernels into phylogenetic regression analysis presents a novel and promising tool for complementing phylogenetic comparative methods. We have integrated this method into Python package named phyloKRR, which is freely available at: https://github.com/ulises‐rosas/phylokrr.

Coincident learning for unsupervised anomaly detection of scientific instruments

Abstract Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexistent), and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called coincident learning for anomaly detection (CoAD), which is specifically designed for multi-modal tasks and identifies anomalies based on coincident behavior across two different slices of the feature space. We define an unsupervised metric, F ^ β , out of analogy to the supervised classification F β statistic. CoAD uses F ^ β to train an anomaly detection algorithm on unlabeled data, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and our motivating task of identifying RF station anomalies in a particle accelerator.

Deep Learning to Authenticate Traditional Handloom Textile

Handloom textile products play an essential role in both the financial and cultural landscape of natives, necessitating accurate and efficient methods for authenticating against replicated powerloom textiles for the protection of heritage and indigenous weavers’ economic viability. This paper presents a new approach to the automated identification of handloom textiles leveraging a deep metric learning technique. A labeled handloom textile dataset of 25,166 images was created by collecting handloom textile samples of six unique types, working with indigenous weavers in Assam, Northeast India. The proposed method achieved remarkable success by acquiring biased feature representations that facilitate the effective separation of different fiber types in a learned feature space. Through extensive experimentation and comparison with baseline models, our approach demonstrated superior efficiency in classifying handloom textiles with an accuracy of 97.8%. Our approach not only contributes to the preservation and promotion of traditional textile craftsmanship in the region but also highlights its significance.