diff --git a/src/prototorch/models/glvq.py b/src/prototorch/models/glvq.py
index 4328b10..1ba9f06 100644
--- a/src/prototorch/models/glvq.py
+++ b/src/prototorch/models/glvq.py
@@ -5,9 +5,10 @@ from prototorch.core.competitions import wtac
 from prototorch.core.distances import (
     lomega_distance,
     omega_distance,
+    ML_omega_distance,
     squared_euclidean_distance,
 )
-from prototorch.core.initializers import EyeLinearTransformInitializer
+from prototorch.core.initializers import (EyeLinearTransformInitializer, LLTI)
 from prototorch.core.losses import (
     GLVQLoss,
     lvq1_loss,
@@ -229,6 +230,51 @@ class SiameseGMLVQ(SiameseGLVQ):
         return lam.detach().cpu()
 
 
+class GMLMLVQ(GLVQ):
+    """Generalized Multi-Layer Matrix Learning Vector Quantization.
+
+    Implemented as a regular GLVQ network that simply uses a different distance
+    function. This makes it easier to implement a localized variant.
+    """
+
+    # Parameters
+    _omega: torch.Tensor
+
+    def __init__(self, hparams, **kwargs):
+        distance_fn = kwargs.pop("distance_fn", ML_omega_distance)
+        super().__init__(hparams, distance_fn=distance_fn, **kwargs)
+
+        # Additional parameters
+        omega_initializer = kwargs.get("omega_initializer")
+        masks = kwargs.get("masks")
+        omega_0 = LLTI(masks[0]).generate(1, 1)
+        omega_1 = LLTI(masks[1]).generate(1, 1)
+        self.register_parameter("_omega_0", Parameter(omega_0))
+        self.register_parameter("_omega_1", Parameter(omega_1))
+        self.mask_0 = masks[0]
+        self.mask_1 = masks[1]
+
+    @property
+    def omega_matrix(self):
+        return self._omega.detach().cpu()
+
+    @property
+    def lambda_matrix(self):
+        omega = self._omega.detach()  # (input_dim, latent_dim)
+        lam = omega @ omega.T
+        return lam.detach().cpu()
+
+    def compute_distances(self, x):
+        protos, _ = self.proto_layer()
+        distances = self.distance_layer(x, protos, self._omega_0,
+                                        self._omega_1, self.mask_0,
+                                        self.mask_1)
+        return distances
+
+    def extra_repr(self):
+        return f"(omega): (shape: {tuple(self._omega.shape)})"
+
+
 class GMLVQ(GLVQ):
     """Generalized Matrix Learning Vector Quantization.