diff --git a/prototorch/models/callbacks.py b/prototorch/models/callbacks.py
index 33b8a32..cea73e8 100644
--- a/prototorch/models/callbacks.py
+++ b/prototorch/models/callbacks.py
@@ -1,12 +1,15 @@
+import logging
 import warnings
+from enum import Enum
 from typing import Optional, Type
 
+import matplotlib.pyplot as plt
 import numpy as np
 import pytorch_lightning as pl
 import torch
 import torchmetrics
-from matplotlib import pyplot as plt
 from prototorch.models.architectures.base import BaseYArchitecture, Steps
+from prototorch.models.architectures.comparison import OmegaComparisonMixin
 from prototorch.models.library.gmlvq import GMLVQ
 from prototorch.models.vis import Vis2DAbstract
 from prototorch.utils.utils import mesh2d
@@ -213,7 +216,7 @@ class PlotLambdaMatrixToTensorboard(pl.Callback):
         # add to tensorboard
         if isinstance(trainer.logger, TensorBoardLogger):
             trainer.logger.experiment.add_figure(
-                f"lambda_matrix",
+                "lambda_matrix",
                 self.fig,
                 trainer.global_step,
             )
@@ -221,3 +224,84 @@ class PlotLambdaMatrixToTensorboard(pl.Callback):
             warnings.warn(
                 f"{self.__class__.__name__} is not compatible with {trainer.logger.__class__.__name__} as logger. Use TensorBoardLogger instead."
             )
+
+
+class Profiles(Enum):
+    '''
+    Available Profiles
+    '''
+    RELEVANCE = 'relevance'
+    INFLUENCE = 'influence'
+
+    def __str__(self):
+        return str(self.value)
+
+
+class PlotMatrixProfiles(pl.Callback):
+
+    def __init__(self, profile=Profiles.INFLUENCE, cmap='seismic') -> None:
+        super().__init__()
+        self.cmap = cmap
+        self.profile = profile
+
+    def on_train_start(self, trainer, pl_module: GMLVQ):
+        '''
+        Plot initial profile.
+        '''
+        self._plot_profile(trainer, pl_module)
+
+    def on_train_epoch_end(self, trainer, pl_module: GMLVQ):
+        '''
+        Plot after every epoch.
+        '''
+        self._plot_profile(trainer, pl_module)
+
+    def _plot_profile(self, trainer, pl_module: GMLVQ):
+
+        fig, ax = plt.subplots(1, 1)
+
+        # plot lambda matrix
+        l_matrix = torch.abs(pl_module.lambda_matrix)
+
+        if self.profile == Profiles.RELEVANCE:
+            profile_value = l_matrix.diag()
+        elif self.profile == Profiles.INFLUENCE:
+            profile_value = l_matrix.sum(0)
+
+        # plot lambda matrix
+        ax.plot(profile_value.detach().numpy())
+
+        # add title
+        ax.set_title(f'{self.profile} profile')
+
+        # add to tensorboard
+        if isinstance(trainer.logger, TensorBoardLogger):
+            trainer.logger.experiment.add_figure(
+                f"{self.profile}_matrix",
+                fig,
+                trainer.global_step,
+            )
+        else:
+            class_name = self.__class__.__name__
+            logger_name = trainer.logger.__class__.__name__
+            warnings.warn(
+                f"{class_name} is not compatible with {logger_name} as logger. Use TensorBoardLogger instead."
+            )
+
+
+class OmegaTraceNormalization(pl.Callback):
+    '''
+    Trace normalization of the Omega Matrix.
+    '''
+    __epsilon = torch.finfo(torch.float32).eps
+
+    def on_train_epoch_end(self, trainer: "pl.Trainer",
+                           pl_module: OmegaComparisonMixin) -> None:
+
+        omega = pl_module.parameter_omega
+        denominator = torch.sqrt(torch.trace(omega.T @ omega))
+        logging.debug(
+            "Apply Omega Trace Normalization: demoninator=%f",
+            denominator.item(),
+        )
+        pl_module.parameter_omega = omega / (denominator + self.__epsilon)